Decision Engine has evolved in the past few decades and new technologies and new ways of thinking about managing decision making has evolved by leaps and bounds. Today we are exploring a few ideas on new features and functionalities decision engines must have to prepare your business for the present and the future.

Open Access Concept

Decision Engines of yesteryears are built as a closed loop meaning that users of these decision engines can only use what’s available on the decision engines platform. There are several reasons why these decision engines choose to be restrictive and does not let the user pick and choose which data provider they want to work with.

First, the decision engine is offered by the third party data providers themselves. If a credit bureau or an identity verification bureau offers a rudimentary decision engine, they will not allow any one else and especially competitors to be integrated with their decision engine. They will always advertise that their product is superior to their competitors and you should only use their product if you want to use their decision engine.

We believe that this is restrictive and prevents competition and constrains the user of the decision engine to optimize their decisioning based on several sources of data. We see this behavior often stemmed from credit bureaus that have a decisioning platform but you can only use their products and not anyone else’s.

We believe an Open Access Concept where the client has a choice is the best way to offer a decisioning platform. If the client builds a credit risk model that leverages three different data providers, then so be it. The decision engine company should integrate with as many data providers as possible to give the client the maximum control and flexibility in terms of leveraging all third parties available to make the best decisions for themselves.

Enterprise Connectivity and APIs 

Many of the current decision engines don’t have APIs or ways to connect to other enterprise systems. They rely on client engineers to write code to interact with the decision engine. This is a costly effort and leaves a ton of room for mistakes and worse compliance risks. 

We believe that decision engines should offer maximum flexibility in terms of interactions with the rest of the clients’ enterprise systems. For instance, if the client has multiple decisioning points with their onboarding system, the decision engine platform should offer customization and APIs to take data into the decision engine from the enterprise system as well as third parties. Then the decision engine should run complex rules or models and return the results back to the enterprise systems and prepare for the next decisioning call.

Some of the existing decision engine platforms can only make one decision run at a time and don't remember what had happened prior. This architecture is built for the old days where the entire application is collected and the submit button then triggers a single decision. 

Today’s applications are risk based, forked at various places depending on the prior information providers and multi-staged. Your decision engine should have ways to make multiple, sequential decisions on the fly and have a history of prior decisions.

Speed and Performance

We can’t emphasize enough the importance of speed and performance when it comes to critical software applications such as your decision engines.

When your decision engine can’t keep up with thousands of requests a minute, it’s time to consider switching to a new platform. If the decision engine is architected in a way that it can only process one request at a time, it will take the decision engine company years to rewrite their platform with modern infrastructure in mind and launch a whole new system.

There’s no amount of engineering or patch work that will speed up the decision making process if the decision engine is architected in a way that is non-scalable. We recommend that when you pick a new decision engine, do a load test to make sure that the decision engine of your choice is future proof and can handle your future growth.

Loan balancing is also an important aspect of a decision engine built for tomorrow’s needs. If your decision engine does not take advantage of multiple CPUs or Servers, you are not set up for future growth and business continuity. Decision Engine is a core part of any bank or lending system and it has no room for failure. Redundancy, load balancing management is key to the backbone of an always-on system such as a decision engine.

Machine Learning, A.I. and Non-Linear Models

Lenders and banks today compete with each other in marketing, customer services as well as the ability to underwrite their prospective customers correctly.

There are so many data points to consider when understanding a customer today. From their credit reports which may contain thousands of variables to banking transactional data which adds another thousand of data points to other behavioral related data points. 

These data points are often blended and mixed into a machine learning or self learning A.I. model or any types of non-linear model that traditional decision engines can’t support. 

Make sure that your decision engine can take in any types of modeling images, complex python code or any other specialty languages such as PMML to produce the best result. We see a lot of banks and lending institutions reduce the complexity of their credit risk models to accommodate deficiencies in their decision engine, this hurts the business and reduces the competitiveness of the bank.

Today’s decision engine should be able to execute any type of mathematical or probabilistic models without compromise. We recommend that you ask these questions and conduct a proof of concept with your decision engine vendor before investing more energy into something that you ultimately have to compromise and diminishes your ability to compete in today’s lending market.

Our Conclusion

2025 is a perfect year to start considering upgrading the most critical piece of your lending stack, your decision engine. Working with industry experts, enterprise software architects with a specialization in decision engines will make your life easier and save a ton of money and time in the long run. 

Also, the ability to finally execute the credit risk models your risk team developed over the years will give you the competitive advantages you’ve been looking for to approve more applicants and convert more clients. Most importantly give the right amount of credit to the right customers to reduce credit loss.  

About LendAPI

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