How AI And ML Technologies Are Impactful For The BFSI Sector?


Today, organizations across the globe rank digital transformation as a top, strategic IT priority – this is especially true in the case of BFSI (Banking, Financial Services, and Insurance) industry. BFSI sector in India is valued at Rs. 81 trillion and is likely to become the fifth-largest in the world by the year 2020 and third-largest by the year 2025. This sector has been very proactive in adopting the latest technologies and addressing the rising challenges of customers by making constant efforts to enhance the existing/new customer experience. As such, they explore, experiment and invest in Data analytics use cases backed by Artificial Intelligence (AI) and Machine Learning (ML), focusing on growing top-line.

Data Analysis comes in the initial phase when banks are trying to make sense of several amounts of unstructured or structured data. Using an AI/ML model enables banks to create a forecasting model for a particular customer which also enables them to create solutions for futuristic problems which the customer might face, through in-depth predictive analysis. Artificial intelligence is the blend of three advanced technologies – machine learning, natural language processing, and cognitive computing. The concept of Artificial Intelligence is to simulate the intelligence of humans into artificial machines with the help of sophisticated machine learning and natural language processing algorithms. The prime motive for the idea of transferring intelligence from humans to machines is to overcome the barrier of human intelligence. AI  is fast evolving as the go-to technology for companies across the world to personalize the experience for individuals.

Moreover, banks have realized the economic advantage of individualization which can bring in considerable cost reduction. Big banks are using cutting edge artificial intelligence techniques by using in-house teams of Data Scientists and Quants for risk assessment, financial analysis, portfolio management, credit approval process, KYC & anti-money laundering systems. On the other hand, small banks can use AI to achieve operational efficiency, accurate recommendations using recommendation engines,  fraud detection using machine learning algorithms and better customer interactions.

Speaking about the role of AI and ML in driving personalisation in banking, Sankat Chauhan, who leads Data Science and Products department at Goals101, a leading FinTech startup said, “While technologies such as Big Data, AI and ML are disrupting the BFSI industry, hyper-personalization is the next big thing, as it will lead to  more resilient, customer-focused bank of the future that incorporates the virtues of non-banking rivals.” Hyper-personalisation is core to any initiative that banks undertake to become more customer-centric. They understand that needs of everyone are unique and want to customize products accordingly.

The technology is getting refined and smarter, allowing more and newer industries to adopt AI for various applications. The banking sector is becoming one of the first adopters of AI. Similar to the other segments, banks are exploring the technology in several effective ways.

”We recently released Synechron’s AI Data Science Accelerators for financial services firms which apply AI to financial services business problems. The Accelerators use correlation and causation analysis to solve complex business challenges by discovering meaningful relationships between events that impact one another and cause a future event to happen.”, says Faisal Husain, Co-founder, and CEO, Synechron

In conclusion, while emerging technologies such as Blockchain, Artificial Intelligence, Robotic Process Automation, Machine Learning, Cybersecurity, etc. look promising, they are always going to be challenges. Customers behavior will transform the industry in the coming years. The companies will need to adapt to new technologies while maintaining profitability in order to stay ahead of the competition.