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AI use case landscape in BFSI: Guide to start your AI journey

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Only 13% of BFSI enterprises are highly satisfied with their AI implementations, while about 50% are moderately satisfied

Globally, BFSI organizations including banks, insurance companies and financial service providers, are at varying levels of AI adoption. Very often, enterprises that are starting their AI journey, quickly jump to an assumed end state and start working on an AI use case without completely understanding its feasibility and/or resultant benefits.  This results in enterprises facing either or both of the following scenarios:

  • Not fully being able to realise the ROI of their AI investments
  • Losing the momentum when their initiative hits a roadblock

To avoid this common pitfall, our recently launched report on Indian BFSI – Unlocking the Transformation Potential of AI proposes a unique way of highlighting AI opportunities in the BFSI sector (across banking, insurance and financial services) through a periodic table of AI use cases. It provides the spectrum of use cases in BFSI industry solving specific value chain challenges that can help enterprises understand the landscape, application areas and come up with an action plan for AI suiting their needs.

The research also highlighted that globally, popular enterprise focus areas for AI implementation in BFSI comprise customer experience, operational excellence and proactive risk management (specifically as a fallout of COVID-19)

Download our report here for accessing the full periodic table and instructions to interpret and use it

Here are some illustrative use cases that are amongst the most popular AI use cases implemented by BFSI enterprises globally across each of the value chain segments

  • Personalized Planning: AI-powered personalization of merchant offers based on customer transactional and behavioural data, as an example Yes Bank recently leveraged an AI based MarTech tool for the launch of its Personal Loan campaign. The AI-built personalized banners targeted customers with relevant interests and offered relevant content at the right time, creating a pin-point personalized communication that is helping Yes Bank reduce their CPA by a staggering 230%.
  • Customer Service/Support: AI-powered chatbot using NLP and contextual data to addressing customer queries / complaints. As an example, HDFC Bank launched EVA (Electronic Virtual Assistant), chatbot built using the latest NLP and AI technologies to offer true power of conversational experience to its customers on all the digital platforms such as the Website, Mobile site and the dedicated portal for the bank’s customers.
  • Claims Investigation: AI driven evaluation of claims using cognitive computing (intelligent OCR), and estimation of the amount to be approved. As an example, ICICI Lombard GIC relies on AI and ML process to cut down on claim processing times.
  • Fraud Detection: AI-powered detection and prediction of fraudulent transactions based on intelligent analysis of buyer / seller behaviour patterns. As an example, PayPal works with, using machine learning technology and statistical models to detect fraud patterns.

BFSI enterprises have been successfully deploying these popular AI use cases as mentioned in the above examples. Customer experience and risk mitigation are the key themes for most of the high-density use case categories. The current COVID-19 crisis has further created multiple complex challenges for banks and financial institutions and driving a stronger focus towards use-cases that enable robust credit risk assessment, cost management, claims investigation, fraud detection / prevention, and stringent customer verification.

Feel free to reach out for any queries/suggestions. Watch out for our next article on how can enterprises prioritize the right use cases with our unique use case prioritization matrix.

To access the full periodic table and detailed use cases download our full report: Indian BFSI: Unlocking the Transformation Potential of AI







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