How financial services industry in India is set for AI makeover

Published: October 2, 2019 2:22:57 AM

The advanced cluster is where AI adoption reaches its pinnacle.

Document analysis and data mining, trading assistance, risk profile assessment, compliance monitoring and fraud detection, and complex financial advisory are some of the well-adopted use cases in this category.

By Aashish Chandorkar & Kamal Misra

Artificial Intelligence (AI), the raison d’etre for cinematic bewilderment in sci-fi movies since ages, is now one of the most important fabricators of contemporary thinking. Several innovations pertaining to AI including chatbots, virtual assistants, machine learning, robotic process automation (RPA) and neuro-linguistic programming (NLP) are being liberally adopted in a crowded marketplace.
In the financial services (FS) industry, the flare-out has been quite phenomenal. Banks and insurers are looking at AI to provide meaningful resolutions to issues such as cost, efficiency, profitability and staffing, among others.

Goldman Sachs has forecast a global money pool worth £26 billion to £33 billion by 2025 in the FS industry alone, primarily attributed to cost savings and new revenue streams on account of the AI revolution. In an internal benchmark developed by the Capgemini Invent India team, there are several interesting insights. Chatbots and virtual assistants engage with the bank customers to provide advice on recurring themes and guide them around personal financial analyses and day-to-day transactions. Machine Learning (ML) too has emerged as one of the most invested-in areas in AI. Document analysis and data mining, trading assistance, risk profile assessment, compliance monitoring and fraud detection, and complex financial advisory are some of the well-adopted use cases in this category.

According to the CapGemini study, AI adoption can be categorised into three clusters: nascent, prospective and advanced. In the nascent cluster, the FS players are at the initial stage of exploring AI technologies to improve internal efficiencies and boost customer experience. The implementation of a chatbot technology is aimed at resolving basic queries about products and services, while RPA helps in automating repetitive tasks. ML is used to analyse customer data and derive key insights.

In the prospective cluster, the technologies are capable of performing more complex exercises, such as banking transactions, monitoring compliance and knowledge management.

The advanced cluster is where AI adoption reaches its pinnacle. The top FS players have made significant investments to build in-house AI innovation hubs, which serve as the focal points to assess, build, refine, test and monitor AI frameworks, algorithms, prototypes, solutions and devices. AI systems here are calibrated to undertake pioneering work in AML / fraud mitigation and pre-emption, user behaviour exploration and complex transactions involving biometrics.

A well-conceived AI ambition in the FS industry should begin with a rigorous maturity assessment (self-diagnosis) phase, where the incumbents need to evaluate areas within their value chain through an optimal mix of quantitative and qualitative probes to identify candidates ripe for an AI makeover. As the study foresees, the onus lies on the banks and insurers to have a tenacious approach towards evaluating the AI narratives that are more pragmatic than ornamental.

Chandorkar is VP & head of Capgemini Invent India; Misra is Director, Financial Services, Capgemini Invent India

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