Helios – an innovative financial risk analysis tool

Yanick Tourn

Yanick Tourn

While demand for credit has risen so have consumer expectations.

Helios gives lenders the edge over their competition, making the credit approval process fast and customizable.

In the world of credit providers, competition for customers increases each year. Companies are offering a wide variety of products, such as loans, credit cards, mortgages, rental guarantees and check cashing. Yet, the public is more and more selective due to the amount of information available at their fingertips to find the best products. For instance, consumers can easily compare interest rates, installments and down payments right from their phones. In this arena, a company’s ability to differentiate themselves from the competition is key.

The importance of risk analysis

Regardless of the credit product, these companies all share the same critical process underlying their operation. Each needs to perform a client risk analysis before they can move forward with credit approval and money disbursement. To put it simply, they have to assure to the best of their knowledge that a prospective client will be able to return what is loaned to them, plus interest. Companies typically have a department in charge of evaluating credit requests, which is a very time-consuming task that sometimes requires manual verification and approval. What’s more, delays in this process can weaken the confidence of a prospective client. After years of working as a developer in the financial services market, I realized that these types of companies were lacking flexibility and speed.

A code-free credit approval alternative

This is where our solution is meant to help. We have designed and built a real-time credit approval engine that allows us to model credit analysis workflows without additional code. The system allows us to unify the results of risk score providers and create very granular offers of credit. We accomplished this by creating a microservices backend architecture with a core API service that acts as a credit analysis and approval engine.

The engine was built with a software design pattern known as strategy, where each strategy resolves a condition or analysis that needs to be approved. Think of it like bricks in a wall that can be composed to create complex scenarios, with reusable algorithms that are combined and arranged by the risk analyst in multiple ways. All of these strategies and conditions are available out of the box and many boil down to equations and variables that are filled by applying the prospective client’s information. These equations were also built in such a way that they can be modified in production, allowing the risk manager to open or close the flow of credit as needed and in real time. 

Another key aspect about this architecture is that it allows subsequent analyses without overlapping those that have been previously made. So in the case where a completely separate analysis must be performed, a new strategy and set of conditions can be implemented that won’t impact the rest already working and in place. We can think of it like silos of analyses that run in parallel.

 

Flexible and scalable

The final product is a robust credit analysis solution that enhances regular score providing services and enables more flexibility and faster credit disbursement. It’s easily integrated with existing accounting tools (via a REST API), cloud-base and easily scalable. Since companies can customise their evaluation configuration and criteria without coding, introducing new credit products is simplified. Products can be tested with existing client databases faster, reducing costs, and allowing companies to more readily expand their operations. 

In contrast, a solution using hard-coded values in its analysis modules would lack flexibility and could take months to go live. Worse yet, it would probably suffer a lot of back and forth between the risk analyst and their IT department to refine the workflow; even after going live it could continue to undergo modifications, many of which wouldn’t necessarily provide added value to end consumers.

Conclusion

Our solution falls in line with how we like to increase company and team performance, as well as resolve bottlenecks: not by making massive or revolutionary changes, but by polishing and improving everyday tasks. In the long term, this makes the most difference.

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