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Banking: Using AI and ML for measurable improvement in customer engagement
Banking: Using AI and ML for measurable improvement in customer engagement

June 25, 2021

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Customer data has always been the secret sauce of success for banks. The bank’s access to deep customer data means that they are at least in theory, better placed to leverage data to fuel their growth. With recent developments, where small businesses and customers are under financial distress due to the pandemic, banks must instead answer the question, “How can we take care of our customers?” To find precise answers for each customer, banks must now turn from data to domain-based Artificial Intelligence (AI) and Machine Learning (ML) that provide effective customer engagement, self-service, and cognitive care.

Our experience shows that banks have not successfully embedded AI and ML solutions into their processes. Admittedly, some have used AI and ML for fraud detection and to predict cash flows, but usually claimed capabilities don’t necessarily result in business gains or customer loyalty. The future of customer engagement lies in AI and ML. Therefore, it is not surprising that research from The Economist Intelligence Unit shows that 86% of banks and insurance companies expect to increase AI-related investment by 2025.[i] Despite the intent, most banks – especially Tier 2, Building Societies & Regional banks – are uncertain about how to deploy AI and ML. Their worries are predictable: “Will my investments go waste? Will I be able to implement solutions within the projected timelines? What is the effort required?” The reason for these questions boils down to the lack of technical know-how.

Banks must apply AI/ML to areas that impact customers and help them overcome their challenges. This means embedding AI/ML into onboarding, servicing, and building customer retention and loyalty. The most immediate impact of this approach will be an increase in profitability, lowered service costs, and reduced customer complaints.

Take, for example, AI-powered delinquency prediction models. They can help provide early signals for financially stressed customers, allowing the bank to not only find means to protect their assets but also to determine how best to assist the customer. Leveraging Open banking data can help determine the risk of customers. This is coupled with self-service applications and intelligent chatbots/ conversational assistants where a real-time offer rendered can guide customers to resolve financial blockers and/or restructure loans.

Customer Intelligence Platform (CIP) for Banking uses an AI/ML-based framework to assist banks to improve their customer engagement. With no upfront investment and an implementation time of six to eight weeks, the platform framework which has the flexibility to support any banking use-case can help banks launch specific intervention to improve CSAT by 5 to 10 percent, increase digital sales by 5 to 6 percent or also reduce fraud by 10 to 15 percent. Technology service providers who have an engagement model that maps commercials to business outcomes, eliminating the cost barrier can prevent most banks from deploying AI and ML.

Improving customer experience and engagement has moved to the heart of banking. As customers become more demanding, CIP provides a competitive differentiator and improves customer retention. The need to address these has never been more urgent.

 

Resouce: [i] https://go.thoughtspot.com/white-paper-economist-ai-future-of-financial-services.html

 

About the Author

Author image
Ranjith Ranadheeran
GM - Banking & Financial Services
ITC Infotech

 

Ranjith is part of the Industry Consulting Group at ITC Infotech and as the Global Capability Leader for Banking and Financial services he is driving conceptualization and creation of market-leading offerings around Digital banking to help Banks and Financial institutions deliver cutting edge Insights led experience solutions to their customers and colleagues. He has 23 years of Technology Consulting & Delivery experience in the Banking & Financial services domain and has helped several banks plan, design, and successfully implement large-scale Core Banking and Digital transformations.


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