Innovation in Lending
AI Lending: Should You Build, Buy, or Rent?
April 6, 2021
In 2020, financial institutions were running not walking to AI projects. And now it’s more of a sprint. Viewed as the key to building a competitive advantage, growing portfolios safely post-COVID, and solving industry challenges like more inclusive lending, adoption of AI in credit risk underwriting is accelerating.
With the business case justified, leaders are focused on answering the implementation strategy question - “Should we build, buy, or rent?” It’s an age-old dilemma but for ml underwriting, the decision is particularly nuanced. Many factors get overlooked which is why the DIY approach results in an ml model that can’t be deployed. In fact, AI project failure rates are at 53%, according to a Gartner report.
AI lending requires a new decisioning framework. Yes, the typical factors (costs, timeline, resources) still apply but the risk, regulatory, costs of getting it right or wrong, and deployment considerations raise the bar and require a new lens. To help lenders through this critical decision stage, we’re providing an updated framework to properly contrast the pros and cons and make the best decision for your organization. In this guide we’ll look at the major factors affecting your decision including: questions, considerations, available options, and the advantages to each approach.
- The Pros/Cons to each AI Approach
- Factors to consider throughout the decision process
- Benefits of getting AI lending right
- Costs of getting AI lending wrong
- How to apply a new framework to your decision