When it comes to the finance industry, particularly lenders need to innovate to win the market share against big finance providers such as banks and credit unions. Based on McKinsey, only 42 to 67 percent of borrowers say they are satisfied with the mortgage process, and banks lagging by about 20 to 30 percent.
Base rate is not always the main reason customers select one provider over another; customer experience is a critical reason behind the decision of choosing a provider. And this is particularly can be a differentiation point for smaller, new-entrant lenders to win the market share against the larger, more established players.
Expand your Market Share by Innovating and Delighting Customers!
If we look at the overall life cycle of a loan from the lending provider perspective, at a very high level, it is something like below:
In each stage, there are many different opportunities to utilize an end-to-end decision automation platform to innovate and delight your customers. These opportunities are across many various financial industries’ use cases.
Not all the use cases can be completely executed only by a decision engine. Still, if we look at the uses cases for decision engine, there will fall into two broad categories:
- Credit Decision Engine
- Tasks Decision Engine
With a breakdown of overall requirements for a credit decision engine into individual modular decision units, lenders can quickly adapt to changes. The impact of changes will be isolated to each decision unit. However, processing a request across different parts of the value chain will require an orchestration capability that can bring together multiple decision units for a specific scenario.
One of the key requirements of a decision engine in the above scenario is the ability to execute different types of decisions. In other words, the decision engine should be able to combine both predictive (data-drive) and prescriptive (rule-based) decisions. On the other hand, the Tasks Decision Engine not also can take care of identifying priorities and asking the right questions from the borrower but also it can automate some manual decisions and tasks which require robotics capabilities.
This approach enables lenders to implement credit engine and task engine in an agile manner, incrementally and iteratively build and deploy rather than a big bang approach of a single gigantic system that does it all.
Scalability and Monitoring
The design of the decision engines should be modular to maximize flexibility and make them scalable. In this modular approach, each decision unit can be deployed as a service. Therefore, they can rely on the power of cloud providers for limitless scalability. For instance, they can use a serverless deployment for individual decision units. Or, they can be deployed on-prem with a scalable architecture (i.e. master-agent architecture), allowing to process more throughputs as required.
Monitoring the decision engine based on business KPI is critical in ensuring the business values are delivered. For example, monitoring of the time-to-close, close-rate, credit-default rate and etc. These metrics will ensure that the implementation meets the expected business KPI. Also, if needed, monitoring allows identifying a new segment and enables the lenders to provide highly customized products to target a new segment.
Adapting modular “credit decision engines” will enable lenders to adapt to regulatory and market changes quickly, which empowers them to provide highly customized products to specific segments while tailoring customer interaction using “tasks decision engine” to provide highly relevant communication and superior customer experience.
Last updated October 4th, 2021 at 12:46 pm, Published September 23rd, 2021 at 12:46 pm