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October 07, 2022

Driving revenue through customer-centric analytics for financial institutions

 

 

 

 

 

Zahir Habib Kotadiya
Associate Director
Crisil

 

Aneesh Kumar Suresh
Senior Consultant
Crisil

 

 

AI and ML unlocking new growth pathways

 

Applications of artificial intelligence (AI) and machine learning (ML) in financial services are suddenly everywhere. As banking itself becomes increasingly online and digital post pandemic, AI/ML solutions are not only proliferating retail credit deeper, but also finding rapid uptake in corporate and commercial segments.

 

Specifically, these twin technological disruptors are carving out a role in a handful of distinct ways: as a differentiator, in deepening client engagement and enhancing client experience, risk control, fraud detection, and increasingly, through strengthening analytics.

 

All of these potentially can drive revenue growth.

 

Let’s take a closer look at how, through the following use cases.

 

customer-profiling

Customer profiling

 

Modern platform-driven AI and ML technologies enable banks to cross-sell, up-sell, acquire new clients and reduce drop-offs more efficiently, through precise customer profiling.  

Because AI can cut through swathes of data in humanly impossible ways, it can help financial institutions analyse the attributes of their borrowers based on their demographics, income and previous banking history, and group them in strategic clusters — all in a trice. The result: differentiated products, and better collection and acquisition strategies.

 

next-best-offer

Next best offer

 

Predictive analytics such as the next best offer (NBO) uses sophisticated rules and algorithms to better predict customer needs, increase customer loyalty, and generate business opportunities to improve wallet share. Crisil’s NBO solutions, for instance, create a unified offer recommendation for customers across banking products, significantly reducing campaign cost, while maximising the up-sell potential. 

 

pricing-analytics

Pricing analytics

 

Pricing plays a significant part in a customer’s credit decision. Price aggregators and comparison platforms have made it easy for customers/lenders to compare prices of the same financial products across different borrowers. Pricing analytics uses AI/ML models to provide pricing comparison for a particular cluster of customers. This helps assess possible revenue gaps and devise smart pricing strategies that could, in turn, increase revenue and profitability

 

supply-chain-solutions

Supply chain solutions

 

Financial institutions often face challenges in gaining a dynamic understanding of customers, including analysing transaction data and counterparties. ML-based supply chain analytics solutions apply complex algorithms to enable transaction analysis and identifying potential opportunities.

 

insights-for-merchants

Insights for merchants

 

The role of banks has evolved from being mere custodians of wealth and loan givers to actually providing actionable insights to their clients through forecasting, customer segmentation, and sales campaign analytics. These AI/ML-enabled solutions lend a competitive edge. 

 

working-capital-management

Working capital management

 

Financial institutions are best placed to advise corporate treasurers on optimising working capital, whether by extending days payable outstanding (DPO), reducing days sales outstanding (DSO) or optimising payment modes. The accuracy of such advice can be greatly augmented by AI/ML backed insights, leading to better cost savings for corporates and increasing customer loyalty.

 

 

Crisil recognises that banks will increasingly need to rely on AI/Ml solutions in the future. Our own platter of solutions are, in fact, tailored to many of the use cases described above.

 

For instance, our AI/ML-based micro-segmentation model builds multi-stage clustering for various customer segments, sharpening the categories by utilising numerical and categorical data.

 

Financial institutions use our pricing analytics that applies complex business logic to atomic level billing information such as relationship, future potential, scale, and riskiness to determine price dispersion and potential to re-price.

 

Crisil’s supply chain analytics solutions, in addition to enabling complex transaction analysis, can spot whitespaces in the existing client relationships and facilitate on-boarding of high-quality new clients through counterparty scoring. It helps clients identify manually transacting customers and move them to digital modes.

 

Our merchant analytics provides AI/ML-based sales forecasting and advanced analytics by combining data from various providers such as Visa and MasterCard. Interactive dashboards and reports provide actionable insights for merchants to manage and grow their business.

 

Financial institutions have also benefitted from better insights on client working capital and acquired new clients through supplier financing using Crisil’s working capital analytics solutions.

 

In all, AI and ML can power financial institutions to provide bespoke customer experience, enhance sales efficiency and drive revenues. Their adoption has simply become a business imperative to stay ahead of the curve.

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