AI for Consumer Banking in the age of Covid-19

5 AI uses in Banks Today

1. Fraud Detection
Artificial intelligence tools improve defense against fraudsters and allowing banks to increase efficiency, reduce headcount in compliance and provide a better customer experience.
For example, if a huge transaction is initiated from an account with an history of minimal transactions – AI can shop the transactions until it is verified by a human.

2. Chatbots
Intelligent chatbots can engage users and improve customer service. AI  chatbot brings a human touch, have human voice nuances and even understand the context of the conversation.
Recently Google demonstrated its  AI chatbot that could make table reservation at a restaurant.

3. Marketing & Support
AI tools have the ability to analyze past behavior to optimize future campaign. By learning from prospect’s past behavior, AI tools automatically select & place ads or collateral for digital marketing. This helps craft directed marketing campaigns
Also see: https://www.techaspect.com/the-ai-revolution-marketing-automation-ebook-techaspect/

4. Risk Management
Real time transactions data analysis when used with AI tools can identify potential risks in offering credit. Today, banks have access to lots of transactional data – via open banking, and this data needs to be analyzed to understand micro activities and access the behavior of parties to correctly identify risks. Say for example, if the customer has borrowed money from a large number of other banks in recent times.

5. Algorithmic Trading
AI takes data analytics to the next level. Getting real time market data/news from live feeds such as Thomson Reuters Enterprise Platform, Bloomberg Terminal etc., and AI tools can use this information to understand investor sentiments and take real-time decisions on trading. This eliminates the time gap between insights & action.

How Machine Learning Aids New Software Product Development

Developing new software products has always been a challenge. The traditional product management processes for developing new products takes lot more time/resources and cannot meet needs of all users. With new Machine Learning tools and technologies, one can augment traditional product management with data analysis and automated learning systems and tests.

Traditional New Product Development process can be broken into 5 main steps:

1. Understand
2. Define
3. Ideate
4. Prototype
5. Test

In each of the five steps, one can use data analysis & ML techniques to accelerate the process and improve the outcomes. With Machine Learning, the new 5 step program becomes:

1. Understand – Analyze:Understand User RequirementsAnalyze user needs from user data. In case of Web Apps, one can collect huge amounts of user data from Social networks, digital surveys, email campaigns, etc.
   
2. Define – Synthesize: Defining user needs & user personas can be enhanced by synthesizing user's behavioral models based on data analysis.
   
3. Ideate – Prioritize: Developing product ideas and prioritizing them becomes lot faster and more accurate with data analysis on customer preferences.
   
4. Prototype – Tuning: Prototypes demonstrate basic functionality and these prototypes can be rapidly, automatically tuned to meet each customer needs. This aids in meeting needs of multiple customer segments.Machine Learning based Auto-tuning of software allows for rapid experimentation and data collected in this phase can help the next stage.
   
   
5. Test – Validate: Prototypes are tested for user feedback. ML systems can receive feedback and analyze results for product validation and model validation. In addition, ML systems can auto-tune, auto configure products to better fit customer needs and re-test the prototypes.

Closing Thoughts

For a long time, product managers had to rely on their understanding of user needs. Real user data was difficult to collect and product managers had to rely on surveys and market analysis and other secondary sources for data. But in the digital world, one can collect vast volumes of data, and use data analysis tools and Machine learning to accelerate new software product development process and also improve success rates.

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.

Data Analytics for Competitive Advantage

Data Analytics is touted as 'THE" tool for competitive advantage.

In this article, I have done a break down of data analytics into its three main components and further listed down various activities that are done in each category.

Three Main Components of Data Analytics

1. Data Management
2. Standard Analytics
3. Advanced Analytics

Data Management

Data Management forms the foundation of data analytics.  About 80% of efforts & costs are incurred in data management functions. The world of data management is vast and complex, it consists of several activities that needs to be done:

1. Data Architecture
2. Data Governance
3. Data Development
4. Data Security
4. Master Data Management
5. Metadata Management
6. Data Quality Management
7. Document & Content Management
8. Database & Data warehousing Operations

Standard Analytics

Standard Analytics is what most businesses have been doing for a long time now. Standard business reporting, Alerts etc., There are several standard analytics functions that business needs for day-to-day activities.

1. Standard Reporting

2. Ad hoc queries

3. Data filtering

4. Alerts

5. Clustering

6. Trend Forecasting

7. Statistical Analysis

Advanced Analytics

Advanced Analytics is what getting all the attention. Big data & analytics using modern algorithms such as Map Reduce. In addition to big data, Advanced analytics includes newer technologies such as AI, ML, RPA etc.

1. Predictive Analytics

2. Prescriptive Techniques

3. Operations Optimization 

4. Simulation Modelling 

5. Machine Learning

6. Artificial Intelligence

7. Robotic Process Automation

8. Deep Learning

Closing Thoughts 

It often assumed that advanced analytics gives competitive advantage. While this statement is TRUE, the foundation of business analytics is in data management and basic analytics - without which advanced analytics will not provide the desired competitive advantages.

One must also note that the level of complexity, costs, and efforts increase exponentially as one moves to advanced analytics. New investments are needed in terms of newer IT infrastructure, software tools, people skills and talent. So companies must be ready to invest to get competitive advantage.

HPE Server Platforms for Big Data Analytics

What will Drive Server Refresh

Looking into the future - Right through Automation & Artificial Intelligence

Its no secret that Innovation & creativity is the ultimate source of competitive advantage. But success of any innovative idea depends on a number of other factors: An energetic leadership, Market growth, Significant investments pool, and a real "can do" spirit of the team.

As I write this article, there are thousands of articles being published on web - which state how robots and Artificial Intelligence will replace humans in workforce.

1. McDonald's is testing a new restaurant run completely by Robots 
2. Robot lands a Plane. 
3. Driverless Trucks will eliminate millions of jobs  
4. Smart Machines will cause mass unemployment  
5. Google's AI beats world Go champion in first of five matches  

Today's news is filled with the hype and fear of mass unemployment due to Automation. Today the hype cycle is almost at its zenith and in this background, I was asked to talk about what kind of jobs & employment opportunities will be there in future.

How automation will help mankind?

Automation is actually an innate feature of mankind. If we look into our past, we as a species have always come up with creative innovation to automate mundane tasks, and every time we did this, the civilization progressed by leaps and bounds.

The first ever automation was creation of canal system - thereby automating the transportation of water. This led to the rise of early civilizations in Indus River valley, Egypt, Babylon & China. Since then, civilization has been making a steady progress to automate simple, repetitive tasks.

Simple machines replaced human labor, trains & cars replaced horse drawn carts, Computers replaced clerks, and the list goes on and on.

From all our learning's, we know that if a task can be automated, it will be automated. There is no way a civilization will be able to stop automation. People have no pleasure in doing repetitive labor and the society will promote automation. Period!

And Yes, Today, People are being replaced by algorithms, machines and artificial intelligence.

What shall humans do?

As automation, artificial intelligence, machine learning and robotics grow in capability, humans doing simple, repetitive jobs will be pushed out of their jobs. So what will humans do?

The answer to this question can be found in history. When canals were invented, farmers found themselves with more time on their hands to increase the land area under cultivation. This led to more food production and this freed up people to create some of early classics of literature. Ramayana, Mahabaratha, Upanishads etc. were written during this time. People built massive temples, pyramids, palaces, forts, statues etc.

Industrial revolution also led to an explosion in arts - mainly paintings, writing of novels, poems, sculpture, building of palaces, classical music etc.

In 20th century and early 21 century, automation led to more creativity in terms of space travel, adventures, film making, music and new machines.

All this points towards only one direction. When humans are freed up from mundane tasks, they will use their free time to harness their creative potential.

Humans are innately creative. Machines, computers & Robots are not creative. Humans are far more creative and this creativity cannot be reduced into an algorithm & automated. This implies that the new generation of humans who are trained to be engineers, doctors, scientists and artists etc. will dream up new things to do, build and explore. Perhaps we may discover how to travel faster than light.

How to prepare for the new future?

The current education system is designed to create a workforce of yesterday, and people who do mundane, repetitive tasks and this education system will have to change first. We as a civilization will have to train the younger generation to be creative, develop expansive & divergent thinking. We need to nurture and flourish the creative side of humans and this will free up the younger generation to be creative and innovative.

Modern workplaces also have to change in a big way. Traditional hierarchical top down management system which pigeonhole people into narrow jobs need to be revamped. Silos need to be broken up and creative ideas must be fast tracked as quickly as possible. Businesses must be willing to take risks on new ideas. For example, Ford Motors recently fired its CEO - even after record breaking sales - because Ford as a business now needs a new business models - far from the one of building cars.

Cost of Failure - A major war & conflict

History also tells us the dark side of automation. Whenever automation ushered in a new era, lot of people had too much free time and when this was not utilized in a positive way, humans resorted to war and violence.

In fact, both world wars were in a way caused by industrial revolution. Industrialized European countries had surplus human labor and young population which had nothing much to do and the countries rushed headlong into a catastrophic war.

Today, we are seeing a massive surge in terrorism from people in Middle East and Pakistan. This is because their governments have failed to utilize their workforce in productive ways. Similarly, USA & China have increased their military spending in recent times - as they are not able to channel their enormous economic resources towards creative work.

Closing Thoughts 

We as a civilization are at the cusp of a new revolution - ushered by Automation & AI. Will this result in a golden era of creativity & innovation or will it result in a catastrophic war?

I cannot predict the future, but I know for sure that if we invest in building a creative & innovative society - we can usher in a golden era, else we are doomed for a devastating war. 

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