How Data Analytics Backed By AI And ML Is Transforming The BFSI Sector



Stephen Hawking, the renowned theoretical physicist once said, “It’s tempting to dismiss the notion of highly intelligent machines as mere science fiction.” The reality is that most AI applications do not have a physical form, but rather “live” in lines of code. The term “AI” includes all technology used to mimic human intelligence, typically falling into one of three subcategories: machine learning, natural language processing and cognitive computing.

The business world is getting transformed and changing rapidly with the digital disruption backed with insight and automation opportunities gained out of Artificial Intelligence and Machine Learning (AI/ ML) enabling Data Analytics. “When Amazon recommends a book you would like, Google predicts that you should leave now to get to your meeting on time, your bank stopping a fraudulent transaction on your credit card, and UBER magically get car of your choice at your doorsteps, these are examples of machine learning over a Big Data stream.”

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The financial industry is a highly regulated and data-intensive industry. With the invent of new entrants like FINTECHs, digital and payment banks, new regulations and change in customer behaviour traditional banking system is facing and feeling an external disruption and tension to reinvent itself and critically examine its business processes to not only get more clients but how to enhance existing customer experience.

The banks are exploring, experimenting and investing in the Data Analytics use cases backed by Artificial Intelligence and Machine Learning (AI/ ML). Below are the five banking areas facing disruption as the banking industry rapidly adopts AI:

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Customer Experience 

The banks are focussing on growing the top line by adding custom services and offering better channel experience to customers. Many banks are introducing chat bots backed by AI abilities which can understand the emotions of the customer analysing their voice and facial expressions and converse accordingly. Through big data and machine learning, these “bots” know how to respond to customer’s questions – from onboarding concerns to transaction-specific questions. Additionally, the technology is capable of managing customer requests and making product recommendations. The key benefit of this advancement is to attract the tech-savvy millennials, known to prefer less human interaction when it comes to financing. Another benefit is to promote less pushy contextual nudges and next best action that customer could take on the basis of their geographical location and the latest financial interaction.

Investment Advisory – Digitization of Advice

Digital-advisors are changing the investment landscape, with AI-powered platforms automating relationship heavy private banking and asset management field. Introduction of Digital-advisors almost entirely eliminates financial advisors and relationship managers from the investing process. Investors no longer have to shell out wealthy or pay hefty fees for something they might not want or need.

Now, Digital-advisors collect information about an investor’s financial goals and the level of risk they’re willing to incur, further this data is integrated with the macroeconomic data then they input this data into algorithms (with quantum computing we will be able to run more sophisticated algorithms in future). In turn, the results are used to offer investment advice to the individual, allowing him/her to make educated investment decisions, or, in many cases, the digital-advisor will fully automate the purchase and management of investments.

Fraud detection and risk management

AI can detect fraud before it happens. Technology can rapidly mimic the thought process of a human analyst to review each transaction in every portfolio at a bank (big data stream). AI enables banks to not only be alerted to potential fraud but also gives them a percentage that depicts the likelihood of a card ever becoming compromised. The appropriate usage of machine learning algorithms could result in the reduction of false positives which not only improves the efficiency of the AI/ ML Fraud Detection process but also helps in improving customer satisfaction.

Regulatory Compliance

 The financial industry is a heavily regulated industry with the rapid evolution of the laws and regulatory mandates. AI can “learn”, remember, and comply with all applicable laws – from KYC and anti-money laundering regulation to laws governing asset management. This results in the elimination of human errors, identification of complex patterns and financial institutions to meet their regulatory obligations.

Equity Predictions

When it comes to big data and pattern recognition, humans are no match for AI. Analysts, investors and other key players on Wall Street have embraced AI as a tool for predicting market movements. AI can evaluate companies’ public remarks (such as on earnings calls), picking up on sentiment analysis (word usage, speech patterns, etc.), which then is used compared with historical data to predict stock performance with near certainty. This advancement benefits the financial institutions and customer simultaneously by taking off the human bias and restriction of sampling as the whole of data (Big Data) is analysed for making such recommendations and predictions.

AI’s disruption in finance is increasing exponentially and it’s geared up for greater economic impact than ever. From better customer experience to extending investment opportunities to the common man to predicting fraud to mitigating investment risks, AI has the potential to not only revolutionize the industry but also to improve the financial health of millions of people in the world.

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by Vijayalakshmi Anandan

The Deep Learning Curve is a technology-based podcast hosted by Vijayalakshmi Anandan - Video Presenter and Podcaster at Analytics India Magazine. This podcast is the narrator's journey of curiosity and discovery in the world of technology.

Neeraj Goyal
Neeraj Goyal, the Head Of Technology, Global Head India at RBS is an accomplished Information Technology executive with over twenty years of experience in the global financial industry. He has both successfully defined and implemented strategies for various business, delivery units & functions at the organizations he has worked in and has rich experience in handling diverse functions across geographies throughout his career. He has been in IT leadership role requiring strong execution skills, building capability & new practices, driving transformation and nurturing talent.

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