Fintech Use Case for Graph Analytics

In my previous blog, I had written about high-level use cases for Graph Analytics. In today’s blog, lets' dive in deeper and take a look at how Fintech companies can use graph analytics to allocate credit to customers & manage risks.

Today, banks and other Fintech companies have access to tonnes of information – but their current databases and IT solutions do not have the ability to make the best use of it. Customer information such as KYC data, and other demographic data are often storied in traditional RDBMS database, while transactional data is stored in a separate database, customer interactions on web/mobile apps, customer interactions data are stored in Big Data HDFS stores, while the data from Social network or other network data about customers are often not even used. 

This is where graph databases such as Neo4J or Oracle Autonomous database etc come into play.  

A graph database can connect the dots between different sources of information and one can build a really cool, intelligent AI solution to make predictions on future purchases, credit needs, and risks. Prediction data can then be validated with actual transactional data to iterate and build better models. 

Graph databases are essentially built for high scalability and performance. There are several open-source algorithms and libraries that can detect components and make connections within the data, you can evaluate these connections and start making predictions, which over time will only get better. 

Importance of Fintech to India

On 8 November 2016, the Government of India announced the demonetization of all ₹500 and ₹1000 banknotes, it set off a wave of Fintech growth in India. Fintech is now mainstream and a critical segment for the future of India's economic growth.

Here are 10 reasons why Fintech is very important to India.

1. Economic Growth
The payment segment has been a major enabler of economic growth. Electronic payments systems added $300B to GDP in 70 countries between 2011-2015, which resulted in ~2.6Million Jobs/Yr
Each 1% increase in electronic payment produces ~$104 B in consumption of goods & services

2. Financial Inclusion
Fintech opens up opportunities for the previously unbanked population to access modern financial instruments. For people living in poverty or at the fringes of the economy, Fintech lowers costs of Financial transactions: Lower cost of credit and other banking services.

3. Speed & Quality of Innovation
Fintech drives improvements in traditional financial services – which will replace legacy systems. Eg: Peer-to-peer lending, Robo advisors, Hi-frequency trading

4. Business Sustainability & Scalability
Fintech has made businesses sustainable & Scalable. The entire e-commerce economy was built on e-payment systems and new business models such as Ridesharing: OLA, UBER, Metro Bikes, etc were developed on Fintech e-payment systems – which allows these businesses to scale and grow rapidly

5. Transparency & Audits
All digital transactions are inherently auditable hence bringing greater transparency into the system. Data sharing in real-time across banks & financial institution reduce fraud risks and reduces the cost of regulatory processes.

6. New Value Streams
New fintech technologies are creating new business opportunities. Bitcoin & other cryptocurrencies have spawned whole new businesses.

7. Market Curation & Structural Transformation 
Fintech technologies are transforming other industries. For example, healthcare record management, Real estate, land registration with Blockchain, etc. This is bringing structural reforms to businesses that were on the fringes of the regulated economy into the mainstream economy.

8. Collaborative Culture
New Fintech businesses are built in collaboration with other businesses. For example, Blockchain is based on open collaboration between members who host the shared ledger.

9. The Scale of the Industry
Fintech has grown from being a niche to mainstream. Today Fintech companies are collectively worth more than $500Billion and directly employ millions of men.

10. Borderless Innovation
Technological innovations in Fintech can be quickly adapted across borders, creating new competition and new opportunities for existing players. This rapid innovation is bringing whole new financial hubs and opening new markets.

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

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

Build Highly Resilient Web Services

Digitization has led to new business models that rely on web services. Digital banks, payment gateways & other Fintech services are now available only on web. These web services need to be highly resilient with uptime of greater than 99.9999%

Building such high resilient Web services essentially boils down to seven key components:

High Resilient IT Infrastructure: 
All underlying IT infrastructure (Compute, Network & Storage) is running in HA mode. High availability implies node level resilience and site level resilience. This ensures that a node failure or even a site failure does not bring down the web services.

Data Resilience:
All app related data is backed up in timely snapshots and also replicated in real time in multiple sites - so that data is never lost and RPO, RTO is maintained at "Zero"
This ensures that Data Recovery site is always maintained as an active state.

Application Resilience:
Web Applications have to be designed for high resilience. SOA based web apps, container apps are preferred than large monolith applications.

Multiple instances of the application should be run behind a load balancer - so that workload gets evenly distributed. Load balancing can also be done across multiple sites or even multiple cloud deployments to ensure web apps are always up and running.

Application performance monitoring plays an important role to ensure apps are available and performing as per required SLA. Active Application Performance Management is needed to ensure customers have good web experience.

Security Plan: 
Security planning implies building in security features into the underlying infrastructure, applications & data. Security plan is a mandatory and must be detailed enough to pass security audits and all regulatory compliance requirements.
Software-Defined-Security is developed based on this security plan and this helps avoid several security issues found in operations.
Security plan includes security policies like: encryption standards, access control, DMZ etc.

Security operations: 
Once the application is in production, the entire IT infrastructure stack must be monitored for security. There are several security tools for: Autonomous Watchdogs, Web Policing, web intelligence, continuous authentication, traffic monitoring, endpoint security & user training against phishing.
IT security is always an ongoing operation and one must be fully vigilant of any security attacks, threats or weaknesses.

IT Operations Management:
All web services need constant monitoring for Availability & Performance. All IT systems that are used to provide a service must be monitored and corrective actions, proactive actions need to be taken in order to keep the web applications running.

DevOps & Automation:
DevOps & automation is a lifeline of web apps. DevOps is used for all system updates to provide a seamless, non disruptive upgrades to web apps.  DevOps also allows new features of web apps be tested in a controlled ways - like exposing new versions/capabilities to select group of customers and then using that data to harden the apps.

Closing Thoughts

High resilient apps are not created by accident. It takes a whole lot of work and effort to keep the web applications up and running at all times. In this article, I have just mentioned 7 main steps needed to build high resilience web applications - but there are more depending on the nature of the application and business use cases, but these seven are common to all types of applications.

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

How AI Tools helps Banks

In the modern era of the digital economy, technological advancements in Machine Learning (ML) and Artificial Intelligence (AI) can help banking and financial services industry immensely.

AI & ML tools will become an integral part of how customers interact with banks and financial institutions. I have listed 8 areas where AI tools will have the greatest impact.

Key Technologies for Next Gen Banking

Digital Transformation is changing they way customers interact with banks. New digital technologies are fundamentally changing banks from being a branch-centric human interface driven to a digital centric, technology interface driven operations. 

In next 10 years, I predict more than 90% of existing branches will close and people will migrate to digital banks. In this article, I have listed out 6 main technologies needed for next gen banking - aka the Digital Bank.

1. MobileMobile Apps is changing how customers are interacting with bank. What started as digital payment wallets, mobile banking has grown to offer most of the banking services: Investments, Account management, Lines of credit, International remittances etc., providing banking services anywhere, anytime!

2.Cloud & API

Mobile banking is built on cloud services such as Open API & Microservices. Open API allows banks to interact with customers and other banks faster. For example, Open API allows business ERP systems to directly access bank accounts and transfer funds as needed. Open API allows banks to interact faster, transfer funds from one back to another etc. In short Cloud technologies such as Open API and microservices are accelerating interactions between banks, and banks & customers, thus increasing the velocity of business.

3. Big Data & Analytics

Big data and analytics are changing the way banks reach out to customers, offer new services and create new opportunities. Today banks have tremendous access to data: Streaming data from websites, cloud services, mobile data and real time transaction data. All this data can be analyzed to identify new business opportunities - micro credit, Algorithmic trading etc.

4. AI & ML

Advanced analytical technologies such as AI & ML is increasingly being used to detect fraud, identify hidden customer needs and create new business opportunities for banks. Though these technologies are still in their early stages, it will get a faster adaption and become main stream in next 4-5 years.

Already, several banks are using AI tools for customer support activities such as chat, phone banking etc.

5. Biometrics & security.

As velocity of transactions increases, Security is becoming vital for financial services. Biometric based authentication, Stronger encryption, continuous real time security monitoring enhances security in a big way.

6. Block chain & IoT

IoT has become mainstream. Banks were early adapters of IoT technologies: POS devices, CCTV, ATM machines, etc.  Block chain technology is used to validate IoT data from retail banking customers. This is helping banks better understand customers and tailor new offerings to create new business opportunities.

Rise of Robo-Advisors!

Robo-advisor  brings down the cost of such services to those investors who otherwise couldn't afford.

Lower cost plus rapid execution of trades based on algorithm gives better results. No wonder people are moving to robot advisors.

Three Practical Use Cases for Blockchain

Why Use Lusture File System?

Lusture File System is designed for a large-scale, high-performance data storage system. Lusture was designed for High Performance Computing requirements – which scales linearly to meet the most stringent and highly demanding requirements of Media applications. 

Lustre file systems have high performance capabilities and open source licensing, it is often used in supercomputers. Since June 2005, it has consistently been used by at least half of the top ten, & more than 60 of the top 100 fastest supercomputers in the world, including the world's No. 2 and No. 3 ranked TOP500  supercomputers in 2014,Titan & Sequoia. 

 Lustre file systems are scalable and can be part of multiple computer clusters with tens of thousands of client nodes, hundreds of petabytes (PB) of storage on thousands of servers, and more than a terabyte per second (TB/s) of aggregate I/O throughput.  This makes Lustre file systems a popular choice for businesses with large data centers, including those in industries such as Media Service, Finance, Research, Life sciences, and Oil & Gas.

Why Use Containers for Microservices?

Microservices deliver three benefits: speed to market, scalability, and flexibility.

Speed to Market
Microservices are small, modular pieces of software. They are built independently. As such, development teams can deliver code to market faster. Engineers iterate on features, and incrementally deliver functionality to production via an automated continuous delivery pipeline.

Scalability
At web-scale, it's common to have hundreds or thousands of microservices running in production. Each service can be scaled independently, offering tremendous flexibility. For example, let's say you are running IT for an insurance firm. You may scale enrollment microservices during a month-long open enrollment period. Similarly, you may scale member inquiry microservices at a different time E.g., during the first week of the coverage year, as you anticipate higher call volumes from subscribed members. This type of scalability is very appealing, as it directly helps a business boost revenue and support a growing customer base.

Flexibility
With microservices, developers can make simple changes easily. They no longer have to wrestle with millions of lines of code. Microservices are smaller in scale. And because microservices interact via APIs, developers can choose the right tool (programming language, data store, and so on) for improving a service.

Consider a developer updating a security authorization microservice. The dev can choose to host the authorization data in a document store. This option offers more flexibility in adding and removing authorizations than a relational database. If another developer wants to implement an enrollment service, they can choose a relational database its backing store. New open-source options appear daily. With microservices, developers are free to use new tech as they see fit.
Each service is small, independent, and follows a contract. This means development teams can choose to rewrite any given service, without affecting the other services, or requiring a full-fledged deployment of all services.

This is incredibly valuable in an era of fast-moving business requirements.

Advantages of Corda - Distributed Ledger Technology

Blockchain & CorDapps Use Cases

R3 Corda Application Architecture

Corda is a distributed ledger platform designed to record, manage and automate legal agreements between business partners. Designed by (and for) the world's largest financial institutions, it offers a unique response to the privacy and scalability challenges facing decentralised applications

Corda's development is led by R3, a Fintech company that heads a consortium of over 70 of the world's largest financial institutions in the establishment of an open, enterprise-grade, shared platform to record financial events and execute smart contract logic.

Corda is now supported by a growing open-source community of professional developers, architects and hobbyists.

What makes Corda different?

1. Engineered for business
Corda is the only distributed ledger platform designed by the world's largest financial institutions to manage legal agreements on an automatable and enforceable basis

2. Restricted data sharing
Corda only shares data with those with a need to view or validate it; there is no global broadcasting of data across the network

3. Easy integration
Corda is designed to make integration and interoperability easy: query the ledger with SQL, join to external databases, perform bulk imports, and code contracts in a range of modern, standard languages

4. Pluggable consensus
Corda is the only distributed ledger platform to support multiple consensus providers employing different algorithms on the same network, enabling compliance with local regulations

Closing Thoughts 

Corda provides the opportunity to transform the economics of financial firms by implementing a new shared platform for the recording of financial events and processing of business logic: one where a single global logical ledger is authoritative for all agreements between firms recorded on it. This architecture will define a new shared platform for the industry, upon which incumbents, new entrants and third parties can compete to deliver innovative new products and services.

Implement Enterprise Grade R3 Corda Platform

6 Key Tools and Techniques for Taming Big Data

Using Big Data across the enterprise doesn't require massive investments in new IT systems. Many Big Data tools can leverage existing and commodity infrastructures, and cloud-based platforms are also an option. Let's take a look at some of the most important tools and techniques in the Big Data ecosystem.

1) Data governance. 

Data governance includes the rules for managing and sharing data. Although it's not a technology per se, data governance rules are enforced by technologies such as data management platforms.
"There's a lack of standards and a lack of consistency," explains Doug Robinson, executive director of the National Association of State CIOs (NASCIO). "There's certain data quality issues: Some of the data is dirty and messy and it's non-standardized. And that increasingly has made data sharing very difficult because you have language and syntax differences, the taxonomy on how information is represented.

... All that is problematic because there's no overarching data governance model or discipline in most states. Data governance isn't very mature in state government nor local governments today, and certainly not the federal government."

Data governance is critical to gaining buy-in from participating agencies for enterprise-wide data management. Before data sharing can begin, representatives of all participating agencies must work together to:

• Discuss what data needs to be shared
• Determine how to standardize it for consistency
• Develop a governance structure that aligns with organizational business & compliance needs

2) Enterprise data warehouse. 

With an enterprise data warehouse serving as a central repository, data is funneled in from existing departmental applications, systems and databases.

Individual organizations continue to retain ownership, management and maintenance of their data using their existing tools, but the enterprise data warehouse allows IT to develop a single Big Data infrastructure for all agencies and departments. The enterprise data warehouse is the starting point for integrating the data to provide a unified view of each citizen.

3) Master data management (MDM) platforms. 

With data aggregated into an enterprise data warehouse, it can be analyzed collectively. But first it has to be synthesized and integrated, regardless of format or source application, into a master data file. MDM is a set of advanced processes, algorithms and other tools that:

• Inspect each departmental data source and confirm its rules and data structures. Identify and resolve identity problems, duplicate record issues, data quality problems and other anomalies 
• Ascertain relationships among data
• Cleanse and standardize data 
• Consolidate the data into a single master file that can be accessed by all participating organizations
• Automatically apply and manage security protocols and data encryption to ensure accordance with privacy mandates

4) Advanced analytics and business intelligence.

High-performance analytics and business intelligence are the brains of the Big Data technology ecosystem, providing government centers of excellence with a comprehensive analytical tool set that leverages extensive statistical and data analysis capabilities. Through the use of complex algorithms, these platforms quickly process and deliver Big Data's insights. Functionality includes the ability to:

• Mine data to derive accurate analysis and insights for timely decision-making
• Create highly accurate predictive and descriptive analytical models Model, forecast and simulate business processes
• Apply advanced statistics to huge volumes of data 
• Build models that simulate complex, real-life systems

5) Data visualization. 

Data visualization tools are easy to use — often with point-and-click wizard-based interfaces — and they produce dazzling results. With simple user interfaces and tool sets, users of advanced business intelligence and visualization tools can easily:

• Develop queries, discover trends and insights
• Create compelling and dynamic dashboards, charts and other data visualizations 
• Visually explore all data, discover new patterns and publish reports to the Web and mobile devices 
• Integrate their work into a familiar Microsoft Office environment
   

6) Specialty analytics applications. 

Multiple analytics techniques can be combined to deliver insight into specialized areas such as:
Fraud, waste and abuse. By detecting sophisticated fraud, enterprises can stop fraud before payments are made, uncover organized fraud rings and gain a consolidated view of fraud risk.

Regulatory compliance. Analytics tools can help agencies quickly identify and monitor compliance risk factors, test various scenarios and models, predict investigation results, and reduce compliance risk and costs.

HR analytics. Hiring is critical to build capabilities quickly. Therefore it becomes important to hire employees who can meet its requirements and fit into its corporate culture. There in lies the challenge: "How to hire someone from outside - who has relevant knowledge needed in banking and who will fit in with the existing corporate culture." This challenge can be solved by using data analytics during the selection process.

Each BU will have several such tools and techniques that are important, but that can't be justified to create data silos. Breaking data silos, combined technology with analytics expertise, new organizational workflows and cultural changes to enable enterprise-wide data management.

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.

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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

Corda - A Next Generation Blockchain Technology

Corda is a distributed ledger platform designed to record, manage and automate legal agreements between business partners. Designed by (and for) the world's largest financial institutions, it offers a unique response to the privacy and scalability challenges facing decentralised applications

Corda's development is led by R3, a Fintech company that heads a consortium of over 70 of the world's largest financial institutions in the establishment of an open, enterprise-grade, shared platform to record financial events and execute smart contract logic.

Corda is now supported by a growing open-source community of professional developers, architects and hobbyists.

What makes Corda different?

1. Engineered for business
Corda is the only distributed ledger platform designed by the world's largest financial institutions to manage legal agreements on an automatable and enforceable basis

2. Restricted data sharing
Corda only shares data with those with a need to view or validate it; there is no global broadcasting of data across the network

3. Easy integration
Corda is designed to make integration and interoperability easy: query the ledger with SQL, join to external databases, perform bulk imports, and code contracts in a range of modern, standard languages

4. Pluggable consensus
Corda is the only distributed ledger platform to support multiple consensus providers employing different algorithms on the same network, enabling compliance with local regulations

Closing Thoughts 

Corda provides the opportunity to transform the economics of financial firms by implementing a new shared platform for the recording of financial events and processing of business logic: one where a single global logical ledger is authoritative for all agreements between firms recorded on it. This architecture will define a new shared platform for the industry, upon which incumbents, new entrants and third parties can compete to deliver innovative new products and services. 

Why Fintech should embrace DevOps

Making your IT operation DevOps friendly can improve Bank's ability to respond to rapid changes in FinTech deployments.

For many years now, the biggest challenges banks face have been "soft" and cultural as much as technical. CIOs and IT directors need to align with business values, nurture security awareness, and cultivate a forward-facing workforce with considerably more urgency than they apply when they look at, say, a migration to IPv6, certifying SOC2 compliance, or calculating the ROI of a shift from spinning disks to flash memory.

The latest wrinkle for IT is how to befriend DevOps talent, or more precisely, how to leverage DevOps capabilities and resources. This introduction describes DevOps, distinguishes DevOps and IT cultures, and concludes with a handful of specific ideas IT should consider in response to DevOps fashions.

What is DevOps?

Make no mistake: A significant fraction of DevOps practice is fashion, in the sense that belief in the benefits of those practices is far more widespread than objective documentation of the same benefits. That doesn't mean that all of DevOps is bunk. It just means that DevOps deserves the same critical scrutiny from IT as functions like data management, legal, or marketing. All of them ultimately have a responsibility to communicate how they promote the organization's mission.

The "DevOps" neologism aspires to capture collaboration between software development (Dev) and IT operations (Ops) skills and experiences. One sign of DevOps' mind-share is that it has inspired even more contrived targets, such as DevSecOps and Lean DevOps.

DevOps is often explained as a contrast to outdated silos of responsibility. In the latter model, programmers code up functionality, then toss their source code "over the wall" to a distinct engineering or operations crew responsible for delivering that functionality at a production level and scale. Ignorance of operations realities often results in developers choosing architectures or coding styles that scale poorly and fail clumsily, and are hard to monitor.

Similarly, operations staff who work without deep programming insight have only blunt tools to attack such real-world requirements as a sudden need to multiply capacity by a factor of 10 or harden an application against hostile actors.

DevOps practitioners should have sufficiently broad and deep perspective to step around such pitfalls. The current hot market for DevOps hires reflects the expectation that DevOps practitioners should be solidly grounded in both development and operations.

Another strong theme in DevOps culture is that practitioners automate and generally treat as software as much of operations as possible. A traditional operations staff might install and test operating system patches during downtime hours, while dedicated DevOps workers are more likely to have hosts under automatic control so they can launch new resources and migrate workloads during business hours. Similarly, a new version of an OS should be just another configuration element to update.

When things work well, DevOps takes credit for reliable, highly scalable results delivered to customers, with rapid turnaround of feature requests.

Hybrid vigor

Those certainly sound like desirable qualities. Can traditional IT hire a few DevOps practitioners and acquire the goodness for itself?

Yes and no, as with any important question. Yes, there's plenty DevOps can bring to traditional IT. There are also numerous hurdles to overcome to avoid wasting IT resources and annoying DevOps adepts.

Part of DevOps reality in 2017 is intimacy with cloud service providers, from Amazon Web Services (AWS), Google's G Suite, and so on. Most DevOps professionals take for granted the rich ecosystems of management and orchestration that have grown around these services. A traditional IT on-premises environment—where a new host or storage unit might take weeks to approve and physically requisition rather than seconds to select and deploy—can upset DevOps hires profoundly.

How can central, traditional IT best welcome DevOps talent? These following four ideas—right target, tech clarity, broad perspective, and API opportunity—will take you a long way toward making the right DevOps impression.

Right target

First, keep the goal clearly in mind: While the title of this piece targets "friendly" relations, that's a metaphor. The real goal is to promote the organization's business mission; "friendliness" or "comfort" are means to that end. A traditional IT department probably should never aspire to the "leading edge" technology turnover that some DevOps relish. At the same time, a traditional IT department can speak frankly about the specific help it needs from DevOps and the opportunities to apply DevOps principles outside the usual DevOps domains. The right candidates prize that sort of honesty and challenge.

Technical clarity

Clarity about the organization's infrastructure plans is also important. Is the department adopting private cloud on-premises? DevOps can help define its configuration. Has the company decided on a hybrid cloud architected to allow loads to migrate from on-site to off-site and back? Hybrid remains a specialized, narrowly understood technology likely to excite many DevOps professionals. Is the company committed to legacy architectures in its own data center?

A smart company can remain with legacy at that level and simultaneously work to virtualize and streamline management of those legacy resources. Good DevOps professionals can recognize that, although on-premises legacy architecture won't help them keep up with the latest AWS releases, integration and modernization projects offer abundant opportunity to apply DevOps principles and tools. Part of recruitment should be to sort out the DevOps candidates you encounter: Does a particular individual hunger to be at the bleeding edge of technology? Is the candidate's reward more in using existing tools to fulfill the organization's needs? While both prospects can equally claim "DevOps" status, the latter is likely to be a better fit for integration in centralized IT.

It's more than just a GitHub account

The DevOps focus is on automation and lifecycle management. While this applies immediately to provisioning, that focus can help traditional IT in other areas, including capacity planning, availability, and business continuity. Certainly the past decade has seen a great improvement in tooling around performance, but much of that tooling is still poorly understood by traditional IT. DevOps will assume Logstash, Kibana, Redis, Elasticsearch, Nagios, Puppet and/or Chef, a messaging bus, and other such tools are available. Even the most traditional IT departments probably need to open up to these basic building blocks. IT probably also needs to support Git or a comparably modern source code management system, along with an integrated issue tracker and source review manager. The department doesn't have to hand everything over to GitHub—but it better offer something almost as good or DevOps careerists will think they're just wasting their time.

Embrace APIs

Join the API gold rush. APIs represent another (at least?) double-edged sword, of course. Plenty of past IT attempts to provide programmability have resulted in ill-conceived uses that made more work for IT, however much the intent was to encourage departments to construct their own robust applications. APIs play a different role in 2017, one DevOps will insist be supported well. Cooperate with DevOps and they will show how APIs can be published and consumed more inexpensively than in the past.

Blended strengths = enormous opportunity

A traditional IT department in a non-technology company does no one any favors by pretending it is DevOps paradise. If it's clear about its goals and plans, though, and ready to move on from break-fix and physical craftwork approaches, it can present its IT plans as great opportunities for the automation and management DevOps does best.

There's no need to view cloud providers and convergence technologies as enemies to traditional IT. Rather, they are simply new challenges that deserve thoughtful response in support of traditional IT's longtime strengths in business continuity management, security routines, identity management, cost accounting, and so on. The opportunity to blend the strengths of DevOps and traditional IT is enormous, at least for those IT decision-makers clear about their plans and resources to support them.