Benefits of Aadhaar Virtual ID

Use Aadhaar Virtual ID to Secure your Aadhaar Details

Considering the privacy of the personal data including the demographic and biometric information mentioned on the Aadhaar card, UIDAI has recently decided to come up with a unique feature, termed as Aadhaar Virtual ID.

The Aadhaar Virtual ID offers limited KYC access providing only that much information which is required for verification rather than offering complete details of an individual's Aadhaar card.

What is an Aadhaar Virtual ID?

The Aadhaar Virtual ID consists of 16-digit random numbers that is mapped to an individual's Aadhaar card at the back end. An Aadhaar card holder using the virtual ID need not submit his Aadhaar number every time for verification purpose, instead he can generate a Virtual ID and use it for various verification purposes like mobile number, bank and other financial documents.

The Aadhaar Virtual ID gives access to the biometric information of an Aadhaar card holder along with the basic details like name, address and photograph that are sufficient for the e-KYC. Unlike in the past, the agency will not know the 12-digit Aadhaar number and other personal details.

Benefits of Aadhaar Virtual ID

1. Complete Privacy of personal Data
   eKYC can now be done without sharing Aadhaar number
   All private information: biometric, DOB, address are private
    
2. User has complete control on sharing Aadhaar ID details
   Only the Aadhaar card holder can generate virtual ID
   Only the Aadhaar card holder can share virtual ID
   Aadhaar Virtual ID expires after a pre-set time, preventing misuse
    
3. Automates all eKYC verification process in the backend
   Simplifies agencies task of individually verifying KYC data
   Web Based verification system is fast and reliable for real time business applications

Big Data Analytics for Digital Banking

Big Data has a huge impact on banking, especially in the era of digital banking.

Here are six main benefits for data analytics for banks.

1. Customer Insights

Banks can follow customer's social media & gain valuable insights on customer behavior patterns
Social media analysis gives a more accurate insights than traditional customer surveys
Social media analysis can be near real time, thus helping understand customer needs better

2. Customer Service

Big data analysis based on customer's historical data, current web data can be used to identify customer issues proactively and resolve them even before customer complains
Eg: Analyzing customers geographical data can help banks optimize ATM locations

3. Customer Experience  

Banks can use big data analytics to customize website in real time - to enhance customer experience.
Banks can use analytics to send real time messages/communications regarding account status etc.,
With Big Data analytics, Banks can be proactive to enhance custoemr service.

4. Boosting Sales

Social media analysis gives a more accurate insights into customer's needs and help promote the right banking products to customers. For e.g., customers looking at housing advertisements and discussing housing finance in social media - are most likely in need of a housing loan.
Data analytics can accurately acess customer's needs & banks can promote right types of solutions.

5. Fraud Detection

Big Data analysis can detect fraud in real time and prevent it
Data from third parties and banking networks holds valuable information about customer interactions.

6. New Product Introduction

Big Data analysis can identify new needs and develop products that meet those needs
Eg: Mobile Payment services, Open Bank APIs, ERP Integration gateways, International currency exchange services etc are all based on data analytics

Aadhaar - A Secure Digital Identity Platform

Secure identity platform helps businesses such as Fintech, Banks, Healthcare, Rental services, etc can use to verify customers' real identities. With a Aadhaar number & a finger print scan, Aadhaar lets businesses accurately identify a customers for trusted transactions.

Digitization has created new business opportunities like Peer-to-peer lending, robo investing, online insurance, online gaming, digital wallets etc. As digitization speeds up the pace of business and needs an equally fast, secure identification system.

Currently, Aadhaar platform has over One Billion Identities - and be used to create new business opportunities and also optimize existing processes. For example, companies can use Aadhaar ID to:

1. Optimize Conversions
A fast & accurate customer verification helps mobile companies or Fintech companies speed up conversion inquiries into paying customers.

2. Deter & Reduce Fraud
Secure identification allows Fintech companies to prevent account takeover and online frauds and also detect & prevent new frauds.

3. Meet Compliance Mandates
Data in Aadhaar provides the necessary data to comply with regulations and directives.

4. Enable new business opportunities
Aadhar ID system enables the new 'sharing' economy, allowing owners to share/rent their assets & earn money

Fintech & Rise of Digital Banks

All around the world, we are seeing a new class of banks: The digital banks. These fintech pioneers are redefining the banking industry by connecting with a new generation of mobile-first consumers.

Digital banks are an online only version of a normal bank offering Savings, Checking Accounts with payment, deposit and withdrawal services – but only through web: PC & Mobile devices.

Proving low cost banking services to a new class of customers: People who are highly mobile, tech savvy and unbanked!

Digital banks offer three main services:

1. Payment Gateways

• A seller service, often provided by e-commerce store or e-commerce enabler
• Authorizes a credit card or online transfer to merchants & businesses
• A virtual Point-of-Sale terminal for online businesses

2. E-Wallets

• Mobile App used to make payments to other mobile wallets
• Digital wallet can be set up to transfer funds to/from a bank account
• Popular banking tool for unbanked.

3. Remittances

• International Money transfers between individuals
• Nearly instant money transfers and low fees to lure customers away from traditional banks
• Uses Bitcoin or crypto currencies to avoid regulatory authorities

Digitalization of Banks and How Blockchain Helps

The core of challenges faced by banking industry today are: Time taken to complete a transaction, Securing customer and bank’s internal data, Compliance with regulations & Fraud detection & prevention.

All these challenges are essentially data & compute related. Once we understand the core data issues, solving them is relatively easy. Blockchain technology is a great solution to solve many of the current banking challenges.

Ten Things One Should know about DevOps

DevOps has taken the IT world by storm over the last few years and continues to transform the way organizations develop, deploy, monitor, and maintain applications, as well as modifying the underlying infrastructure. DevOps has quickly evolved from a niche concept to a business imperative and companies of all sizes should be striving to incorporate DevOps tools and principles.

The value of successful DevOps is quantifiable. According to the 2015 State of DevOps Report, organizations that effectively adopt DevOps deploy software 30 times more frequently and with 200 times shorter lead times than competing organizations that have yet to embrace DevOps.

They also have 60 times fewer failures, and recover from those failures 168 timesfaster. Those are impressive numbers and define why succeeding at DevOps is so important for organizations to remain competitive today.

As the DevOps revolution continues, though, many enterprises are still watching curiously from the sidelines trying to understand what it's all about. Some have jumped in, yet are struggling to succeed. But one thing's certain — it's a much greater challenge to succeed at DevOps if your CIO doesn't grasp what it is or how to adopt it effectively.

HPE Server Platforms for Big Data Analytics

What will Drive Server Refresh

The Digital Workplace

Today's digital workforce demands a secure, high-speed Wi-Fi connectivity. Pervasive wireless access to business-critical applications is now expected wherever users work. Wireless LANs (WLANs) need massive scalability, uncompromising security, and rock solid reliability to accommodate the soaring demand. 

Embracing a mobile first digital workplace

Designing and building a high-performance workplace needs a wireless network, and the applications that run on them, is where services from Hewlett Packard Enterprise (HPE) excel.

With Aruba wireless technology can deliver a mobile first workplace which connects to Microsoft Skype for Business and Office 365, making the transition to a digital workplace a seamless process.  

The digital workplace enables people to bring your own (BYO)-everything with pervasive wireless connectivity, security, and reliability. This enables IT to focus on automation and centralized management. The mobile first workplace will be simpler to manage and maintain. 

Benefits include:

• Higher productivity with fast, secure, and always-on 802.11ac Wi-Fi connectivity

• Lower operating expenditures (OPEX) through reduced reliance on cellular networks

• Better user experiences  

• Reduce infrastructure cost in an all wireless workplace by 34%

• Increase business productivity

• Reduce hours spent on-boarding and performing adds, moves, and changes

Blockchain & CorDapps Use Cases

How to Design a Successful Data Lake

Today, business leaders are continuously envisioning new and innovative ways to use data for operational reporting and advanced data analytics. Data Lake is a next-generation data storage and management solution, was developed to meet the ever increasing demands of business & data analytics.

In this article I will explore some of the existing challenges with the traditional enterprise data warehouse and other existing data management and analytic solutions. I will describe the necessary features of the Data Lake architecture and the capabilities required to leverage a Data and Analytics as a Service (DAaaS) model, characteristics of a successful Data Lake implementation and critical considerations for designing a Data Lake.

Current challenges with Enterprise Data Warehouse 

Business leaders are continuously demanding new and innovative ways to use data analysis to gain competitive advantages.

With the development of new data storage and data analytic tools, the traditional enterprise data warehouse solutions have become inadequate and are impeding maximum usage of data analytics and even prevent users from maximizing their analytic capabilities.

Traditional data warehouse tools has the following shortcomings:

Timeliness 
Introducing new data types and content to an existing data warehouse is usually a time consuming and cumbersome process.

When users want quick access to data,  processing delays can be frustrating and cause users to ignore/stop using data warehouse tools, and instead develop an alternate ad-hoc systems  which costs more, waste valuable resources and bypasses proper security systems.

Quality
If users do not know the origin or source of data  - currently stored in the data warehouse, users view such data with suspicion and may not trust the data. Current data warehousing solutions often store processed data - in which source information is often lost.

Historical data often have some parts missing or inaccurate, the source of data is usually not captured. All this leads to situations where analysis results provide wrong or conflicting results.

Flexibility 
Today's on-demand world needs data to be accessed on-demand and results available in near real time. If users are not able to access this data in time, they lose the ability to analyze the data and derive critical insights when needed.

Traditional data warehouses "pull" data from different sources - based on a pre-defined business needs. This implies that users will have to wait till the data is brought into the data warehouse. This seriously impacts the on-demand capability of business data analysis.

Searchablity
In the world of Google, users demand a rapid and easy search for all their enterprise data. Many of the traditional data warehousing solutions - do not support an easy search tools. Thus customers cannot find the required data and it limits users' ability to make best use of data warehouses for rapid on-demand data analysis.

Today's Need

Modern data analytics - be it Big Data or BI or BW require:

1. Support multiple types (structured/unstructured) of data to be stored in its raw form - along with source details.
    
2. Allow rapid ingestion of data - to support real time or near real time analysis
    
3. Handle & manage very large data sets - both in terms of data streams and data sizes.
    
4. Allow multiple users to search, access and use this data simultaneously from a well known secure place.
    

Looking at all the demands of modern business, the solution that fits all of the above criteria is the Data lake.

What is a Data Lake? 

A Data Lake is a data storing solution featuring a scalable data stores - to store vast amounts of data in various formats. Data from multiple sources: Databases, Web server logs, Point-of-sale devices, IoT sensors, ERP/business systems, Social media, third party information sources etc are all collected, curated into this data lake via an ingestion process. Data can flow into the Data Lake by either batch processing or real-time processing of streaming data.

Data lake holds both raw & processed data along with all the metadata and lineage of the data which is available in a common searchable data catalog. Data is no longer restrained by initial schema decisions, and can be used more freely across the enterprise.

Data Lake is an architected data solution - on which all the common compliance & security policies also applied.

Businesses can now use this data on demand to provide Data and Analytics as a Service  (DAaaS) model to various consumers. ( Business users, data scientists, business analysts)

Note: Data Lakes are often built around a strong scalable, globally distributed storage systems. Please refer my other articles regarding storage for Data lake

Data Lake: Storage for Hadoop & Big Data Analytics

Data lake - Solving the challenge of Big Data Integration

Understanding Data in Big Data

Uses of Data Lake

Data Lake is the place were raw data is ingested, curated and used for modification via ETL tools. Existing data warehouse tools can use this data for analysis along with newer big data, AI tools.

Once a data lake is created, users can use a wide range of analytics tools of their choice to develop reports, develop insights and act on it. The data lake holds both raw data & transformed data along with all the metadata associated with the data.

DAaaS model enables users to self-serve their data and analytic needs. Users browse the data lake's catalog to find and select the available data and fill a metaphorical "shopping cart" with data to work with.

Broadly speaking, there are six main uses of data lake:

1. Discover: Automatically and incrementally "fingerprint" data at scale by analyzing source data.
    
2. Organize: Use machine learning to automatically tag and match data fingerprints to glossary terms. Match the unmatched terms through crowd sourcing
    
3. Curate: Human review accepts or rejects tags and automates data access control via tag based security
   
4. Search: Search for data through the Waterline GUI or through integration via 3rd party applications
    
5. Rate: Use objective profiling information along with subjective crowdsourced input to rate data quality
    
6. Collaborate: Crowdsource annotations and ratings to collaborate and share "tribal knowledge" about your data
   

Characteristics of a Successful Data Lake Implementation

Data Lake enables users to analyze the full variety and volume of data stored in the lake. This necessitates features and functionalities to secure and curate the data, and then to run analytics, visualization, and reporting on it. The characteristics of a successful Data Lake include:

1. Use of multiple tools and products. Extracting maximum value out of the Data Lake requires customized management and integration that are currently unavailable from any single open-source platform or commercial product vendor. The cross-engine integration necessary for a successful Data Lake requires multiple technology stacks that natively support structured, semi-structured, and unstructured data types.
    
2. Domain specification. The Data Lake must be tailored to the specific industry. A Data Lake customized for biomedical research would be significantly different from one tailored to financial services. The Data Lake requires a business-aware data-locating capability that enables business users to find, explore, understand, and trust the data. This search capability needs to provide an intuitive means for navigation, including key word, faceted, and graphical search. Under the covers, such a capability requires sophisticated business processes, within which business terminology can be mapped to the physical data. The tools used should enable independence from IT so that business users can obtain the data they need when they need it and can analyze it as necessary, without IT intervention.
    
3. Automated metadata management. The Data Lake concept relies on capturing a robust set of attributes for every piece of content within the lake. Attributes like data lineage, data quality, and usage history are vital to usability. Maintaining this metadata requires a highly-automated metadata extraction, capture, and tracking facility. Without a high-degree of automated and mandatory metadata management, a Data Lake will rapidly become a Data Swamp.
    
4. Configurable ingestion workflows. In a thriving Data Lake, new sources of external information will be continually discovered by business users. These new sources need to be rapidly on-boarded to avoid frustration and to realize immediate opportunities. A configuration-driven, ingestion workflow mechanism can provide a high level of reuse, enabling easy, secure, and trackable content ingestion from new sources.
    
5. Integrate with the existing environment. The Data Lake needs to meld into and support the existing enterprise data management paradigms, tools, and methods. It needs a supervisor that integrates and manages, when required, existing data management tools, such as data profiling, data mastering and cleansing, and data masking technologies.

Keeping all of these elements in mind is critical for the design of a successful Data Lake.

Designing the Data Lake

Designing a successful Data Lake is an intensive endeavor, requiring a comprehensive understanding of the technical requirements and the business acumen to fully customize and integrate the architecture for the organization's specific needs. Data Scientists and Engineers provide the expertise necessary to evolve the Data Lake to a successful Data and Analytics as a Service solution, including:

DAaaS Strategy Service Definition. Data users can leverage define the catalog of services to be provided by the DAaaS platform, including data onboarding, data cleansing, data transformation, data catalogs, analytic tool libraries, and others.

DAaaS Architecture. Datalake help data users create a right DAaaS architecture, including architecting the environment, selecting components, defining engineering processes, and designing user interfaces.

DAaaS PoC. Rapidly design and execute Proofs-of-Concept (PoC) to demonstrate the viability of the DAaaS approach. Key capabilities of the DAaaS platform are built/demonstrated using leading-edge bases and other selected tools.

DAaaS Operating Model Design and Rollout. Customize DAaaS operating models to meet the individual business users' processes, organizational structure, rules, and governance. This includes establishing DAaaS chargeback models, consumption tracking, and reporting mechanisms.

DAaaS Platform Capability Build-Out. Provide an iterative build-out of all data analytics platform capabilities, including design, development and integration, testing, data loading, metadata and catalog population, and rollout.

Closing Thoughts  

Data Lake can be an effective data management solution for advanced analytics experts and business users alike. A Data Lake allows users to analyze a large variety and volume when and how they want. DAaaS model provides users with on-demand, self-serve data for all their analysis needs

However, to be successful, a Data Lake needs to leverage a multitude of products while being tailored to the industry and providing users with extensive, scalable customization- In short, it takes a blend of technical expertise and business acumen to help organizations design and implement their perfect Data Lake. 

Managing Big data with Intelligent Edge

The Internet of Things (IoT) is nothing short of a revolution. Suddenly, vast numbers of intelligent sensors and devices are generating vast amounts of data that contain potentially game-changing information.

In traditional, data analytics, all the data is shipped to a central data warehouse for processing in order to get strategic insights, like all other Big data projects, tossing large amounts of data of varying types into a data lake to be used later.

Today, most companies are collecting data at the edge of their network : PoS, CCTV, RFID scanners, etc. IoT data being churned out in bulk by sensors in factories, warehouses, and other facilities. The volume of data generated on the edge is huge and transmitting this data to a central data center and processing it in a central data center turns out to be very expensive.

The big challenge for IT leaders is to gather insights from this data rapidly, while keeping costs under control and maintaining all security & compliance mandates.

The best way to deal with this huge volume of data is to process this data right at the edge - near the point where data generated.
 

Advantages of analyzing data at the edge  

To understand, lets consider a factory.  Sensors on a drilling machine that makes engine parts - generates hundreds of bits of data each second. Over time, there are set patterns of data. Data showing vibrations, for example - it could be an early sign of a manufacturing defect about to happen.

Instead of sending the data across a network to a central data warehouse - where it will be analyzed. This is costly and time consuming. By the time the analysis is completed and plant engineers are alerted, there may be several defective engines already manufactured.

In contrast, if this analysis was done right at the site, plant managers could have taken corrective action before defect occurs. Thus, processing the data locally at the edge lowers costs while increasing productivity.

Also keeping data locally improves security and compliance. As all IoT sensors - could potentially be hacked & compromised. If data from a compromised sensor makes its way to the central data warehouse, the entire data warehouse could be at risk. Avoiding data from traveling across a network prevents malware from wreaking the main data warehouse.  If all sensor data is locally analyzed, then only the key results can be stored in a central warehouse - this reduces cost of data management and avoid storing useless data.

In case of banks, the data at the edge could be Personally Identifiable Information (PII), which is bound by several privacy laws and data compliance laws, particularly in Europe.

In short, analyzing data on the edge - near the point where data is generated is beneficial in many ways:

• Analysis can be acted on instantly as needed.
• Security & compliance is enhanced.
• Costs of data analysis are lowered.

Apart from these above mentioned obvious advantages, there are several other advantages:

1. Manageability:

It is easy to manage IoT sensors when they are connected to an edge analysis system. The local server that runs data analysis can also be used to keep track of all the sensors, monitor sensor health, and alert administrators if any sensors fail. This helps in handling a wide plethora of IoT devices used at the edge.

2. Data governance: 

It is important to know what data is collected, where it is stored and to where it is sent. Sensors also generate lots of useless data that can be discarded or compressed or eliminated. Having an intelligent analytic system at the edge - allows easy data management via data governance policies.

3. Change management: 

IoT sensors and devices also need a strong change management( Firmware, software, configurations etc.). Having an intelligent analytic system at the edge - enables all change management functions to be off loaded to the edge servers. This frees up central IT systems to do more valuable work.

Closing Thoughts

IoT presents a huge upside in terms of rapid data collection. Having an intelligent analytic system at the edge gives a huge advantage to companies - with the ability to process this data in real time and take meaningful actions.

Particularly in case of smart manufacturing, smart cities, security sensitive installations, offices, branch offices etc. - there is a huge value in investing in an intelligent analytic system at the edge.

As conventional business models are being disrupted. Change is spreading across nearly all industries, and organizations must move quickly or risk being left behind their faster moving peers. IT leaders should go into the new world of IoT with their eyes open to both the inherent challenges they face and the new horizons that are opening up.

Its no wonder that a large number of companies are already looking to data at the edge.

Hewlett Packard Enterprise makes specialized servers called Edgeline Systems - designed to analyze data at the edge.  

Big Data as an Organizational Center of Excellence

Introduction - Managing Data Across Large Enterprise

Today, enterprises are looking for Integrating Data to Support Analytics Based Decision Making and are still facing massive challenges. The biggest challenges they have is that data is located in silos, and yet large volumes of data are being generated are still managed in silos.

Thanks to new technologies such as IoT, there is an abundance of data being generated. Enterprise information systems, Networks, Applications and Devices that churn out huge volumes of information are awash in Big Data.

But enterprises are unable to make best use of this data as their internal organization — the network of people & business process are operating in isolation and many analytics efforts that only take into account information from a single silo - that delivers results in a vacuum - thus prevent them from making better business decisions.

The best way to lower costs of managing big data and leveraging this data for actionable insights - is to have a  pan-enterprise Big Data strategy.  (Also see Getting Your Big Data Strategy Right)

The best way to solve this problem is to create Big Data as an organizational center of excellence. This special group that can cut across silos, take ownership of all data and create new opportunities for operational excellence with Big Data.

Big Data as an Organizational Center of Excellence

Managing all data across the entire organization will improve efficiencies and services while lowering costs of mining this data.

Organizational BU's can now use this centralized data for actionable insights that can help them make better business decisions.

As business leaders recognize a pan-enterprise Big Data effort provides much more meaningful insights because it's based on an integrated view of the business. Yet they are faced with the challenges of siloed information systems. The current IT implementations and business process prevent data sharing and access to data.

In this article, I will present a solution to address the challenges of data silos and data hoarding.  As a enterprise wide solution, I shall present a new CoE for Big Data - a solution for breaking down data silos and discuss the key benefits of the solution.

Big Data CoE will take ownership of all data and present an integrated data for data analysis improves performance across the global enterprise. Big Data CoE can thus deliver long-term return on investment (ROI), enabling business leaders to develop a solid data for making better Analytics Based Decisions.

Understanding Data Silos and the Pitfalls of Data Hoarding 

Before I dive into the details of Big Data CoE, let take a quick look at data silos and dangers of data hoarding.

In large, global enterprises, individual business units (often referred to as BU's) are notorious and pervasive. Data silos are born in this toxic environment - where individual BU budgets procurement processes create data silos.

Group IT resources which are often scarce and are often designated for specific BU functions.   Each individual BU has no financial incentive to share data and IT resources. As a result, data collect by each BU becomes siloed and data is hoarded in each BU's IT systems and is not shared.

This results in organizational deficiencies where the entire organization suffer from redundant systems and inefficient decision-making. Because enterprise information systems remain segregated, data is walled up in departmental databases and applications. With valuable assets trapped in silos, BU's are unable to leverage data to improve processes, workflow or service delivery.

While data silos are created by operational and technical challenges, data hoarding is a result of insular agency cultures that encourage autonomy and self-reliance, as well as stringent compliance mandates for securing data, especially in this era of risks from data leaks/breaches and liability lawsuits.

In this environment, "business data" becomes "OUR DATA." Data hoarding trumps openness and sharing.

The impact of data silos and data hoarding is quite devastating. Without data sharing across BU's, each BU maintains its own view of business and there is no holistic view of a consistent global business. There is no integration of relevant data and this leads to missed opportunities and wrongful expenses and wastage, delays in discovering fraud, waste & misuse of money.

In addition, critical decisions are made with partial data - that leads to unproductive staff and duplicated efforts - which leads to wastage. Budgets are drained by the cost of managing and maintaining complex and redundant information systems, applications and system interfaces.

Finally, data silos and data hoarding weaken security & compliance efforts. It's harder to ensure the security and privacy of information as it moves among computer systems and databases, and can lead to noncompliance with critical regulations. (PCI-DSS, SOX, etc)

A Holistic Model: Big Data CoE 

Envision a pan-enterprise model for managing Big Data as an organizational center of excellence, A competency center whose core focus is to manage Big Data across the organization and provide right set of tools & infrastructure for business analytics.

Big Data CoE is created with a common focus to manage data and develop new technologies and architectures to analyze big data for making better business decisions

When Big DataCoE model is applied to the enterprise, we can instantly see the following benefits:

• Data is treated as an organizational asset.
  Treating data as an organizational asset, CoE develops and fosters a collaborative environment for users across BU's to meet and exchange ideas, discuss new projects and share best practices.
  
   
• Data is managed separately from IT in terms of strategy, organization, resources, purchasing and deployment. This frees up enterprise IT from handling the challenges of Big Data. Data can reside in-house systems or on public clouds.
  
   
• Distinct processes are developed for collecting, consolidating, managing, linking, securing, sharing, analyzing, archiving, publishing and governing data.
  
   
• Analytical expertise is shared among individual departments, which relieves them of the burden of independently recruiting their own talent and developing unique solutions.
  
   
• Data is aggregated, shared and analyzed using a single, enterprise-wide data platform. A unified system of enterprise data management and analytics tools ensures seamless integration of data repositories with analytic tools, provides a common user experience with access to all types of enterprise data and builds end user engagement in data-driven decision-making.

Unlike siloed data, an enterprise wide approach to data provides all BU's with a single version of the truth. With this integrated, holistic view, decision-making involves all relevant, consistent data, regardless of data ownership.

Creation of Big Data CoE is Transformative

The biggest benefit of Big Data CoE is that it transforms business operations.

Big Data CoE leads to business transformation:

1. More efficient Enterprise. When used at the enterprise level, Big Data can reduce fraud, waste and misuse of funds, Enhance communication and coordination between BU's, Improve management of Big Data, and Identify key business trends across the entire enterprise.
    
2. Faster & Better Decision Making. A complete view of each BU's data, CoE can offer the most appropriate data management & analytical services by identifying patterns that might otherwise be missed. They can also eliminate data processing errors and duplicative data entry. Provide consistent procedures and processes eliminates waste of valuable time and speed up decision making.
    
3. Stronger compliance efforts. When data management is integrated, it makes it easier to implement data compliance mandates across organization. Entire enterprise data can be made secure and compliant even when it is shared across BU's.
    
4. Cost reduction. More efficient data management, analytics & workflows, compliance, security and  service delivery - all lead to cost reductions. By consolidating data analytics efforts under a single CoE, additional savings can be realized because departments don't have to procure and manage their own systems or hire department-specific data scientists and analysts.

To realize the benefits of an enterprise approach to Big Data, Enterprises must adopt a comprehensive approach that leverages appropriate tools and techniques.

Closing Thoughts  

Big Data can provide tremendous business advantages by for improving business productivity,  business decision making and service delivery. But Big Data can only live up to its true potential only if analytics programs are implemented thoughtfully and skillfully.

The strategic use of Big Data and data analytics technologies and tools requires considerable innovation, creative thinking and leadership.

The "silo mentality" has to be broken up and data needs to be shared across the enterprise as a common asset. Having a CoE manage all of Big Data allows enterprises to holistically manage, share and leverage data for faster decision making and service delivery.

Big Data CoE helps enterprises and its BU's to rethink and retool the way they collect, manage, archive and use data. CoE will enable Bus to work together and share information - this leads to better decision-making, faster service delivery and develop an enterprise wide approach to managing and using Big Data.

Artificial Intelligence is the core of Fintech

As an expert in Fintech and big data analytics, I had to write this blog - which is essentially a transcript of my talk in Hewlett Packard to group of very talented & experience folks.

Big data is THE enabler of artificial intelligence. Financial industry generates a whole lot of data and this data forms the basis for powerful analytical tools that use Artificial Intelligence technologies which automates a whole lot of decision making.

What is AI?

At its highest level, Artificial Intelligence is an intelligent technology that leverages historical data and applies what is learned to current contexts to make predictions. AI combines various related terms: machine learning, natural language processing, deep learning, predictive analytics, etc.

Fintech & AI

In the current era of digitization and customer empowerment with mobile Internet, Banks and financial services companies are coming under intense pressure to compete with new age Fintech companies.

Fortunately, big banks and financial firms have huge amounts of customer data this can be leveraged along with newer tools to create & deliver exceptional and memorable experiences using technology.

After decades of research in AI technologies, AI is ready for prime time. According to a report by AI solutions market is estimated to reach $153 billion by 2020.

Today, AI is poised to become a key enabler of modern CRM solutions to Banks. Today many banks & firms use automated communication tools such as ChatBots to reach out to customers.

80% of executives believe artificial intelligence improves worker performance. 

Lets now take a look at these new tools and technologies.

AI:  One of the key technology trends upending the financial industry

Today's data-saturated world offers immense opportunities for financial institutions that know how to put this data to use by structuring it in the right way. As a result, an increasing number of financial institutions are adopting Artificial Intelligence (AI) to better serve their customers and increase their business growth. The explosive growth of structured and unstructured data, availability of new technologies such as cloud computing and machine learning algorithms, rising pressures brought by new competition, increased regulation and heightened consumer expectations have created a 'perfect storm' for the expanded use of artificial intelligence in financial services.

Artificial Intelligence with its advances in computing power, the ability to store and process Big Data, and instant access to advanced algorithms offers many opportunities for the financial sector. Harnessing Artificial Intelligence enables financial institutions to spot nonstandard behavior patterns when auditing financial transactions or to assess and analyze thousands of pages of tax changes. AI is destined to be the perfect tool to empower financial institutions' service efforts with genuinely intelligent tools to cut the time spent on handling lending requests, providing financial consultancies or opening bank accounts. Utilizing intelligent tools enable financial organizations to provide excellent customer experience across different channels. To remain relevant in a technology-driven world, financial pros will have to learn to combine their efforts with these intelligent tools.

Use cases of Artificial Intelligence in financial institutions

The financial sector is embracing Artificial Intelligence and machine learning to stay afloat and win over the digitally native customers. Harnessing the disruptive technology offers plenty of opportunities for financial institutions including: Customer support, transactions and helpdesk, data analysis and advanced analytics, underwriting loans and insurance, repetitive tasks and  performance, automated virtual assistants and Chatbots. Intelligent tools augment the capabilities of financial pros enabling them to easily identify customers' preferences and react with insight and emotional intelligence, which is essential for the development of meaningful customer relationships.

By leveraging intelligent tools, financial institutions and banks become technologically sophisticated and capable of meeting the financial needs of digitally savvy customers. Utilizing AI allows financial pros to analyze customers' buying patterns and red flag any irregularities and take preventive measures. Aienabled tools allow for making better risk decisions and conducting more accurate risk credit assessments.

Predictive scoring

Employing predictive scoring allows financial professionals to predict credit-related behavior, defaulting on loan payments, occurring an accident, client churn or attrition. Scoring backed by intelligence empower financial institutions to recognize creditors who will pay back a loan from those who will not pay based on the credit application's data. Financial pros can effectively apply scoring in forecasting of credit risk before granting a loan or when the loan is already granted.

Banks apply predictive scoring when forecasting the risk for a granted loan or when selecting optimal debt collection activities by assessing the credit related behavior.

Also See: 

1. Decision support with use of predictive models comparing to application of common sense rules or rules prepared by expert gives profit higher by 10-30%. 
2. 35% of executives say their decision relies mostly on internal data and analytics. 
3. By adopting an intelligent-computing program, some banks have experienced a 10% increase in sales of new products, a 20% savings in capital expenditures, a 20% increase in cash collections, and a 20% decline in churn. 

Computer intelligence with human touch

With the current virtual assistants (VC) and Chatbots revolution, along with the tremendous growth of messaging and social media apps, there is a great opportunity for organizations to optimize processes and deliver better customer experiences.

Chatbots are personal assistants that leverage messaging apps or outbound messaging and can run continual analysis of the information that is needed by the customer to ensure they get the relevant information through their preferred channel. Today's intelligent Chatbots and Virtual Customer Assistants can even take into consideration the context of the discussion and provide answers to several questions while analyzing the whole communication thread.

Natural language processing (NLP) is another intelligent tool for uncovering and analyzing the "messages" of unstructured text by using machine learning and Artificial Intelligence. It allows for providing context to language, just as human brains do. As a result, financial pros can gain a deeper understanding of customers' perception around their products, services and brand. NPL can be employed by customer service agents to more quickly route customers to the information they need.

Use Case: Nina, a customer service web-assistant developed by Swedbank, processes around 30,000 conversations focusing on 350 different queries each month. Nina had a first-contact resolution rate of 78% in the first three months of its operation.

Also See

1. The use of virtual customer assistants (VCAs) will jump by 1,000% by 2020. ()
2. The virtual digital assistant (VDA) market is estimated to reach $15.8 billion worldwide by 2021 with unique active consumer VDA users growing to 1.8 billion, and enterprise VDA users rising to 843 million.
3. Research firm MarketsandMarkets predicts the NLP market will reach $13.4 billion by 2020, a compound annual growth rate of 18.4 percent. 

Next best action for financial pros

Another benefit that AI offers for financial institutions is prescriptive analytics integrated with the next best action approach. Next best action is a customer-centric paradigm that considers various actions that can be taken for a specific contact and decides on the 'best' one. The next best action is determined by the customer's needs and interests on the one hand, and the business objectives and policies on the other.

Leveraging innovative technology can provide financial pros with recommendations about what steps they should take next to achieve a specified goal, such as the highest possible revenue or the highest level of engagement.

Utilizing the most innovative predictive decision-making technology can significantly improve the accuracy and effectiveness of financial activities. Through applying analytics, financial pros are able to better understand customer needs and drive higher customer value. Tech savvy financial pros are armed with the right tools to choose the most relevant and efficient process flow and ensure that an offered product or service meets the customer's needs.

Embracing intelligent tools allows financial institutions to focus on predicting customer behavior in order to drive value, managing multichannel interactions, and operating as an insight-driven business.

Utilizing predictive analytics to better understand your customers

Artificial Intelligence provides financial pros with the intelligent tool of predictive analytics that augments their capabilities to create exceptional and memorable customer experiences. Analyzing all internal and external customer data and converting it into actionable insights allows for not only providing real-time advice and solutions, but also anticipating future financial needs on a customer level. Banking analytics, combined with cognitive computing, provides financial pros with the ability to know each customer, provide customers with personalized offers, making one-to-one relationships a possibility. It accelerates financial institutions' ability to create individualized experiences for customers and realize tangible business benefits.

Applying predictive analytics enables financial institutions to:

• Deliver meaningful and emotionally satisfying digital experiences.
• Optimize product portfolio and enable numerous cross-sell and upsell opportunities to highly refined audiences.
• Convey personalized and relevant marketing messages to a specific audience at a specific stage of life.
• Create a complete customer view (360-degree) to personalize all customer touch points, while capturing digital data on a customer's preferences.

Also See:

1. 47% of organizations across industry now use predictive analytics to support business insight for risk purposes.
2. Artificial Intelligence Will Drive The Insights Revolution - Forrester
3. How is big data analytics transforming corporate decision-making?  

Closing Thoughts  

In the age of the customer and intelligent technology, financial institutions and banks are under increased pressure to pay attention to technological developments such as AI, and are quickly adapting to these changes. Intelligent tools along with Big Data offer financial institutions a huge opportunity to deliver exceptional and memorable experiences. Furthermore, Customer Relationship Management (CRM) solution backed with these sophisticated tools provide financial pros with the right blend of technology and human touch. An intelligent CRM solution ensures the complete view of your customers, keeping all their preferences and interests in the centralized repository accessible for financial pros, and enabling them to provide exceptional customer experience. With the AI-powered tools financial pros can exponentially enhance the customer journeys with more personalized approach. Embracing Artificial Intelligence is at the forefront of propelling the financial institutions through the digital age of the customer.

=======
Key Learnings:

1. Artificial Intelligence can become a success enabler of modern financial institutions

2. Computer intelligence use cases financial institutions can employ to become more
Customer centric

3. AI powered tools such as predictive scoring, advanced analytics, virtual assistants and Chatbots can help financial institutions and banks win over digitally native customers

How Automation will change the face of Indian Banks

Today, I had to visit a SBI branch near my house. I had three banking tasks: Deposit a cheque - which was a payment received from a Postal Savings account to the bank account; Transfer funds from the bank account to Public Provident Fund Account and update the passbook to know the bank balance.

This task in a public sector bank branch took nearly 60 minutes of my time, and I had to interact with 3 clerks and one service manager!

This experience made me think on how the upcoming digital transformation will change the face of Indian Banks. As an example, the same task that took me an hour today could be done in few minutes on a digital platform and without any human intervention!

Indian banks operating in conventional systems use tedious human oriented process. I need to fill out a form - where all information is filled out twice - one copy for the bank, one for me! The form is then verified by a clerk and then re-verified by a service manager, and then it takes 3-5 working days for money to move from one account to another!

With automation, 60% of jobs in bank branches can be eliminated. Traditional jobs like passbook updating, cash deposit, verification of know-your-customer details, salary uploads are also going digital increasing job redundancies. Most of the work done at branches will be done by IT systems, and Indian banks are at the inflection point where technology will rapidly improve efficiency and replace humans from performing mundane clerical jobs.

Banking sector was among the big job creators in recent years, but in next 3 years, Banking sector in India will see a decline in number of jobs. Just like ATMs that eliminated the need for bank tellers, new banking apps will eliminate most of the clerical jobs in any bank.

Not all Branch Jobs will be Lost

While I was at the bank, I two elderly gentlemen who wanted my help in getting a "service token!" These long time users of bank accounts are still not comfortable with using digital technologies. Even simple 'self service token generation' is a tough task for them.

India as a society is still not ready for a 100% digital banking. Lots of Indians are digitally illiterate, and the complex banking rules and forms intimidate them. Therefore these users still prefer to sit across the table with a bank employee and get their banking tasks accomplished.

Size of this customer segment is still substantial, but will decline rapidly. This implies that Indian banks will operate branches for a long time to come, but for banks to be relevant and profitable, the total value of transactions per employee will have to increase. This implies that a lot of back end jobs will have to be automated, and moving customers to digital platforms.

The nature of Indian customers has slowed down the transition from people-driven to IT driven processes. However, technological development has not slowed down and constant innovation in technology has made online banking easier. This has also led to a slowdown in the hiring of  branch staff at banks, though banks are hiring people with IT skills to drive automation.

HDFC bank for example, saw staff strength fall from 90,421 in December 2016 to 84,325 in the quarter ended March 2017. At the same time, it has expanded its network to 4,715 branches, from 4,520 a year earlier, ATMs to 12,260 from 12,000.

Hire Younger Talent

As banks brace for the digital revolution, Banks will have to infuse their workforce with a lot of younger talent - who are more comfortable in embracing new FinTech solutions. Large public sector banks will be forced to re-balance their workforce by offering voluntary retirement scheme for older staff and usher in a younger, digitally savvy talent pool.

Major Savings is in Backend Processing

Banks can save lot of costs by automating high cost operations such as loan processing. Today with high speed data analytics & AI tools almost 95% of loan requests can be processed automatically, thus eliminating expensive human labor - which also aids in speeding up loan approvals - which inturn helps improve productivity.

A team of 400-500 programmers can automate house loan approval process & that will eliminate 10000's of jobs at banks. Artificial intelligence & data analytics can replace loan underwriters, the IT systems can underwrite loans on the spot. This increases employee productivity in a big way, while reducing operational costs.

Similarly, routine tasks like salary processing will get automated. In fact all low-end back office jobs in banking sector will be automated.

This transformation means all the low skilled workers do not have a bright future! They will have to re-skill or perish!

Impact on Real Estate Costs

After labor costs, rentals on bank branches form the next highest costs for Indian banks. With Automation, the need for a large number of branches will reduce substantially. Some banks do not need as many branches as they have today.

Global banks like Citi, HSBC, etc have already consolidated the number of branches. Looking at the trend in US & Europe, where number of bank branches have shrunk by 20% in last 5 years, I expect a similar trend in India - but with a difference. The reduction of bank branches will be limited to urban areas only, while Indian banks will still add new branches in non-urban areas and the size of branches will reduce rapidly. Overall, I expect the total square footage of branch space will reduce 20-25% in next 5 years, while the total number of bank branches will increase.

Even in rural areas, Branches will need smaller footprints - and the focus in these branches will be to train customers to use digital online platforms. The branch staff will help customers learn and use digital systems - i.e., marry digital technology with human touch.

Changing face of Indian Banks

New banks like small finance banks like Au Financiers, Equitas or Ujjivan will use an army of  people to expand in rural areas, but this army of people will not sit & operate in a typical branch. Instead, they will be like foot soldiers, traveling to customer locations and providing banking services via mobile platforms.

Just like micro-finance companies & cell-phone companies that changed how people borrow, these newer small finance banks will change how people will use banking services. These banks can leverage a large army of small business owners in rural areas to offer human touch to rural customers - with digital platforms.  Customers can walk to any member of this rural service army and get their banking services.

Customers will now recognize the bank by the mobile apps, rather than the physical branches or the employees.

Closing Thoughts 

Banking sector in India will thrive in next 10-20 years and will employ millions. But it will not be the same way as today. Banks will expand branches and increase the reach of its distribution network, but it will be aided in a big way by IT.

New, exciting, & high paying jobs in Banks will be in IT as Banks transform to be a IT driven, software based banking services company.

The new face of the bank will be the mobile app - from which the entire banking transactions can be completed.

Also see: 

Disruptive influence of FinTech on Indian Banks

Disruptive influence of FinTech on Indian Banks

Indian banking & financial services industry has endured a tumultuous months following November 8th 2016 announcement of demonetization.

Banning of high value currency notes and the subsequent cash shortage and financial crisis is still taking their toll on banks. Indian banks were already under tremendous pressure due to bad loans and are now facing increased demands from retail customers. Many banks are still struck with slow & archaic online payments, with users needing to type in user names, passwords, 16 digits from the credit card and more. It is no wonder that the public trust and confidence in Indian banks is arguably at an all time low.

It is no surprise that consumers and businesses alike have moved enmasse to newer financial services which are enabled by FinTech!

FinTech is a catch-all term for the nascent revolution in the financial services space. Mobile payment systems that use technology and Internet platforms to offer a wide range of financial services. FinTech now offers a genuine alternative to traditional banking and payment systems offered by financial services firms such as Visa & Master card!

Indian customers & businesses are tired of the oligopoly of the state owned banks and duopoly of Visa/Mastercard in payments services sector.

Mobile payment systems offer an exciting, democratizing development which offers tools and services needed to meet the demands of vast majority of Indian small businesses and consumers. This denotes a major paradigm shift in banking and it will disrupt existing financial systems.

Disruption

Last few months post the demonetization, Fintech based payment systems in India - PayTM, MobiKwik, Freecharge, mPesa etc., have moved aggressively to get new customers and businesses, thus loosening the vice-like grip of banks & card payments. PayTM has made a huge splash in the 2016 and has changed the way consumers view payments.

Small Merchants in India have openly embraced FinTech payment services - mainly because of ubiquity of smart phones. More and more Indians are using smart phones, which enables banking and payment transactions to be completed electronically.

This transition to mobile payments also coincided with rapid adaptation of 4G data services.  In last 4 months alone, more than 125 Million users have taken 4G data services. This enabled rapid movement to digital payments. Rural businesses & merchants are accepting mobile payment through services like PayTM, MobiKwik,  UPI, etc.

Given the increasing usage of smart devices and mobile payment methods, there will be rapid growth of FinTech industry. I think in the near future we will see everything being paid for with our mobiles– for example paying Rs 10 for cup of coffee!

The movement towards mobile payment systems with newer payment companies using FinTech is just the beginning. Eventually customers will stop using credit/debit cards or cash, opting for mobile payments instead. This denotes the first move away from traditional banking transaction.
Given the increasing usage of smart devices and other contactless payment methods to complete transactions, business to consumer growth seems a natural direction for the FinTech industry.
In the next phase, people will start investing from their mobile platforms. Mobile payments systems will evolve to offer interest bearing investment opportunities - in form of fixed term deposits, Recurring Deposits or Mutual Funds etc., which can be accessed directly from user mobile devices.

As technology and consumer tastes continue to evolve, the market for financial services must keep pace, and learn to evolve. Newer FinTech companies will lead this new revolution.

Traditional banks, insurance & financial companies will struggle to change and adapt to this new paradigm. Banks in India will particularly find it hard to change because their customer experience management, based on legacy systems and legacy thinking, is lagging behind.

FinTech companies on the other hand have no technical debt, and they design the solution based on end user experience, therefore FinTech companies will have the upper hand when it comes to building better services.
  
Closing Thoughts

In the short term, FinTech in India will evolve & grow around the consumer banking space - with focus on consumer banking, making it easier for consumers to pay, and making it easier for small businesses to transfer money to other business entities. FinTech companies will unbundle banking & financial services and pick the 'cherries out of the cake', focusing on high-margin, highly scalable product and service areas, while leaving the commoditized or low margin services to banks.

Banks have the choice of either becoming 'platform utilities' or turning themselves into FinTech companies and building up their own FinTech ecosystems via various FinTech partnership and innovation models, and corporate venturing strategies.

The paradigm has shifted. The influence of FinTech is sure to be felt for years to come. Thanks to a perfect storm of changing banking rules, market forces and business cultures, FinTech has proved to be a disruptive force in Indian banking circles, a trend which looks set to continue well into 2020.

Fintech - The Success Factors

In today's changing world, for Banks to remain competitive, Banks must commit to transforming themselves into fully-digitalised businesses and must operate like a true technology driven company.  

From our perspective, 10 key aspects that have defined success in Fintech companies that Banks needs to adopt -

1. Customer experience - Fintech companies constantly focus on improving customer experience and learn from every interaction. This is the mantra of success.

2. Digitalization - Create a "stretch vision" for digitalization blue print and make CxO's accountable for it. The "stretch vision" are based on what digital outcomes and it should not be constrained by digital interactions or by current technologies or by as-is scenario. These stretch visions should become inspiration for new innovations.

3. Technology is a key enabler – Fintech's are changing the world through agile platforms and new technologies. Banks must acquire new capabilities and technologies and need to invest in promising technologies. Banks must be willing to take on technology risks and bet on emerging technologies to build new parallel solutions/services - instead of the relying only on traditional ways of relying on "Tested & Proven" solutions.

4. Talent Management - Banks must invest on right talent, if needed on a global scale. Companies have to hire people from other regions/nations & en masse. For example, Indian banks should not shy away from hiring people in Silicon valley. Banks should note that they are hiring digital skills not industry experience. To succeed, hire the right talent and change internal staffing processes to do so – if required.

5. High Performance Culture - Success in new technology development depends on having the right digital talent. Key employees must be protected from "business as usual" attitudes and teams need to be built on high performance attitude. This implies changing existing HR polices,  benefits & operating models etc. For some banks, it makes sense to up a separate business unit that nurtures Fintech initiatives. This unit operates outside the "Traditional Banks" and does not suffer from traditional organizational hierarchies and limitations. in order to increase collaboration, productivity and "mind-shift".

6. Data driven decisions - Banks must shift their mode of operations from process driven to data-driven. Data analytics drives every activity and provides insights to decisions. Data analytics and modelling will have to continually evolve and keep adding to its value proposition. Banks must embrace live testing in Fintech as they adopt agile development and work in "live-beta" environments - in order to increase the pace of innovation and acceptability.

7. Create Eco-System through collaboration - Banks must build collaboration, partnership, and open transactions to succeed. This essentially calls for a big mind-shift as Banks now need to look at the entire ecosystem and collaborate with the best in the world to create a global value chain. New regulations impose these in forms but this a great way to successes and this helps compete/collaborate with startups.

8. Agility with Zero-waste attitude- Banks must learn to manage development projects like a start-ups with complete agile process. Along with agility, zero-waste based budget approach for technology development is key. All expenses must be aligned with the value creation and not based on effort spent.

9. Scalability – Solutions built must be scalable across the organization and value chain. This implies that all new technologies being developed must have scalability built into its core DNA and solutions developed may be able to rapidly scale for success. Startup fintechs today keep scalability as key requirement and don't face downtime issues. Banks need to fast track this process. For example, PayTM was able to scale up by more than 1000% in one month after demonetization in India seamlessly.

10. Challenge Status Quo - Banks must challenge everything. Status quo is not acceptable when dealing with Fintech which impacts all functions, products, business units and locations. Banks must examine & change (as needed) all aspects of the business: embracing both customer-facing and back-office systems and processes and be more innovative.

What is driving Fintech?

Fintech has taken the banking world by storm. Customers are embracing Fintech services rapidly and wholeheartedly. Digitization and Fintech is impacting all aspects of banking, investments & finance. Fintech's impact will be huge and will fundamentally transform the finance industry.

Today, I had open discussion with a bunch of managers at HPE, a group of really smart folks with advanced degrees from tier-1 colleges, on what is driving the rapid move towards Fintech and here is what came out of this discussion.

Digitalisation is a Mega trend 

Digitalisation is happening everywhere and is going far deeper than the cost-saving potential from innovative IT or even developing new revenue streams. Digitisation is fundamentally changing people behaviour.

Digitisation is being driven by three main forces

1. Customer experience
2. Technology enablement
3. Cost Savings

1. Customer experience

Customer experience is really driving Fintech adoption. Customers love the ease of using mobile, web for banking interactions - when compared to walking into a physical bank branches. The ease and the enhanced customer experience of using technology solutions over the good old way of interacting with banks/insurance/finance companies.

Customers are really pushing banks towards  digitalisation. Customers are the leaders in embracing Fintech and are dragging banks into it. Today, customers expect a seamless multi-channel experience - be it from a physical branch or on a mobile app, A consistent service levels that are of global standards.

Customers who got used to the convenience of Fintech are judging banks in how well banks are able to meet their needs. For example, it takes 8 distinct steps to transfer money online when compared to 2 steps in PayTM.

2. Technology enablement

Smart phones and Mobile Internet is rapidly influencing customer behaviour. The ease of connectivity with 4G technology and increased bandwidth has enabled customers to access technology enabled services.

Technology is no longer the realm of large banks and upper class. Customers of all social classes are now very comfortable using mobile, Internet technology and are using it everyday. During the demonitization exercise in India in November 2016, people from all walks of life started to embrace Fintech - right from street vendors, small shop owners, handy men like plumbers etc.

Technology has empowered people and people learnt to harness the power to technology to their benefit.

3. Cost Savings

Unlike technology developments of the past where banks embraced new technology to lower costs, today customers are embracing new technology to save costs for themselves. Customers are unwilling to pay the bank transaction charges for sending money or for loan approvals etc. Customers are using Fintech to reduce their cost of using financial services.  For customers there are two forms of cost savings - time & fees. Fintech provides both benefits, it lowers transactional costs to near zero, and gives instantaneous service which saves time.

Today, Banks are at a disadvantage with their high cost structures and customers are tired of paying banks for basic financial services. Hence it is cost savings to customers that is driving Fintech.

Closing Thoughts 

Fintech can cut costs by up to 90% while rapidly improving turnaround times.  Digitalisation customers to do more with their money and increases the velocity of transactions. This implies that banks need to evolve and rapidly embrace Fintech to make best use of it - allow customers to interact often, provide new innovative services and create better customer experiences. Else customers will move to those banks or new age finance companies that gives them what customers need.


To succeed in the world of digitalisation, Banks must develop a clear strategy that puts customers first - ahead of every single internal processes and costs. This may require redsigning current processes - while looking holistically at end-to-end customer experience, and must go beyond basic regulatory requirements.

The goal must nothing short of "total customer delight", and must traverse across channels for the customer - as a continuous engagement with customers across partners in financial services.