Innovating new financial products using AI

 

Executive Summary

Banks and other financial institutions have been using AI to strengthen their security environment, improve their regulatory compliance and automating several internal processes such as Customer Onboarding, Credit Appraisal etc.,

The strength of any new technology lies in enabling rapid growth of adopters. Traditionally, financial institutions developed products around customer segments, regulatory requirements and periodic market research. Savings accounts, loans, credit cards, insurance products and investment products are generally designed as standardized offerings with limited ability to adapt to individual customer circumstances.

Artificial Intelligence (AI) is fundamentally changing this model. AI enables financial institutions to move from product-centric banking to customer-centric, intelligence-driven financial services.

The combination of Artificial intelligence, machine learning, generative AI, real-time analytics, behavioural data and automated decisioning enables financial institutions to create a new generation of products that are

  •          Personalized
  •          Context-aware
  •          Dynamically priced
  •       Risk-sensitive

  • Embedded into Customer-journeys
  • More inclusive
  • More profitable


 


·  AI can therefore become not merely an enabler but an engine for creating, pricing, distributing, monitoring and continuously improving financial products.

This whitepaper presents a framework for using AI to innovate financial products across deposits, lending, SME finance, Agricultural finance and embedded finance. It also proposes an AI financial product innovation factory that financial institutions can use to systematically identify opportunities, design products, validate risks and launch products.

 

Introduction

The financial services industry is undergoing a transition from digitization to intelligence. The first wave of digital banking is almost over with each department in the financial institution automating their repetitive tasks using technology.

The second wave created mobile banking, internet banking, digital payments and automated service channels. This innovation focused on the mode of delivery of financial services thereby increasing the convenience for customers.

The next wave is AI-native financial services.

AI allows a financial institution to understand customers at a much deeper level by combining the following information

·         Transaction history

·         Demographic information

·         Account behaviour

·         Cash-flow patterns

·         Loan repayment behaviour

·         Digital interactions

·         Market conditions

·         Customer objectives

·         Product usage

·         Life-stage indicators

·         External and alternative data

The result is an opportunity to create products that are not simply pre-defined packages of financial services. Instead, financial products can become dynamic financial solutions curated to each customer to meet their specific financial needs/goals.

Traditional financial product development has several limitations including restriction to specific customer demography (for example : senior citizen savings), static pricing resulting in slow innovation. Product performances are evaluated after launch and there is a significant time delay in the management making the changes based on the evaluation.

Financial products vary by the organization depending on their operational area, financial stability, customer base, demand scenario etc.,

 

AI Financial Product Innovation Framework

This framework comprehensively looks at all the data available for the financial institution on a continuous basis.

 

 



 

AI can help in each of these phases. Discover focuses on identifying unmet needs of the customers. Based on that a new set of products can be designed with multiple variants. Simulate will focus on addressing revenue, cost, risk, liquidity and profitability parameters of the financial institution.  Validate will allow the institution to check the regulatory compliance, credit and operational risk and fairness of the products. Launch will focus on the deployment channels such as Mobile, Internet, Branch, Relationship managers, Embedded finance or digital market places.

Learn and optimize will help the institution measure the effectiveness of the product in the market place and provide opportunities to continuously optimize price, features and offers.

AI generated financial products

One of the most important emerging opportunities is AI-assisted product composition.

AI can combine existing financial capabilities to suit each category of customers.

AI engine could determine the following based on customer’s financial profile

·         Deposit requirement

·         Credit limit

·         Interest rate

·         Insurance Coverage

·         Investment Allocation

·         Fees

·         Eligibility

·         Review Frequency

The resulting product may be different for every customer while still operating within the institution’s approved product architecture.

Goal based savings

AI can infer or explicitly capture financial goals such as

·         Education

·         Home purchase

·         Travel

·         Emergency fund

·         Retirement

·         Business expansion

The system can calculate the required periodic contribution and dynamically adjust the recommendations based on actual cash flows.

  

AI-driven lending products

Lending is likely to be one of the largest areas of AI-enabled financial product innovation.

Imagine, if the question can be modified from



                                               To 
 


 

 

 

 

 


 AI can dynamically create personalized loan structure based on borrower characteristics and cash-flow patterns. BankSoft – AI lending module enables financial institutions to offer this to their customers through mobile/internet banking. Borrowers can quickly assess their loan eligibility and get a customized offering to suit their financial needs.

Cash-Flow based lending

Instead of relying primarily on historical financial statements, AI can analyse actual cash flows.

For an SME, AI could examine

·         Bank transactions

·         Receivables

·         Payables

·         Tax information based on consent

·         Inventory cycles

·         Seasonal patterns

This results in a dynamic working capital facility offered to businesses to meet their cash flow requirements.

The facility limit could increase when business cash flows strengthen and reduce when risk increases.

 

AI-Based Interest rate innovation

Pricing can become one of the most powerful AI capability enabling the financial institution to establish itself as a differentiator in the marketplace.

The AI models can support and arrive at unique personalized pricing.

 

 



 

The financial institution should maintain the governing rules for pricing including floors, ceiling, risk-based pricing rules, fairness constraints etc. AI can then dynamically compute and establish personalized offers to business owners.


Conclusions

Artificial Intelligence is enabling Financial Institutions to move from standardized products to intelligent customized financial solutions. The most significant opportunity is not simply automating existing banking products but to invent new products that was previously impossible.

AI makes it possible to

·         Understand customers continuously

·         Detect unmet financial needs

·         Create personalized products

·         Dynamically price products

·         Learn from customer behaviour

The winning financial institutions of the next decade are the ones rapidly designing, launching and optimizing financial products at scale to meet specific customer needs.

 

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