Whitepaper: Harnessing The Power Of Data For Effective Fair Lending
Author: Arun Iyer
Arun is a Business and IT leader with 23+ years of experience in the Banking and Financial Services industry in areas such as product management, operating model design, business architecture, process optimization and information management. Arun has a keen interest in building innovative solutions and at HEXANIKA he is responsible for Product Strategy and Roadmap while also enabling the Business and Sales functions.
In his previous role as Global Head of Financial Services at Tech Mahindra he was responsible for incubating platform-based solutions and driving digital initiatives in areas such as RPA, machine learning and business intelligence. Before that he was Director – Business Consulting at Cognizant Technology Solutions responsible for areas such as strategy and planning, consulting, solution conceptualization, pre-sales, product alliances, and operations. He has domain expertise in Capital Markets, Asset and Wealth Management and Regulatory Compliance.
Harnessing The Power Of Data For Effective Fair Lending
A Robust Approach to Fair Lending Compliance
When it comes to Fair Lending, lenders should take a three-pronged approach;
- Comply – Meet the specified regulatory mandate of annual data submission for loan information in the HMDA and CRA reporting requirements
- Assess – Identify and analyze the risks in key areas of Fair Lending namely Underwriting, Pricing, Redlining, Steering and Marketing. Additionally, perform a comparative analysis of firm’s fair lending performance against peer organizations
- Prevent – Use advanced technologies like machine learning to predict credit decision outcomes based on historical data and compare it against actual approvals / denials and identify disparities and refine existing models and processes. Additionally, use alternative data such as customer complaints to identify any potential discriminatory lending scenarios