Modern decisioning solutions have changed the game for the finance industry. Organizations are revolutionizing their operations and boosting their competitive advantage by shifting from clunky legacy decisioning tools to advanced decisioning software.
But how do you find the best decision engine for your business? To help you make the right decision, this article summarizes the most important criteria risk experts evaluate when choosing a decision engine – and includes a cheat sheet to fast-track your evaluation process.
The benefits of using a modern decision engine
Unlike traditional decisioning solutions, modern decision engines are equipped with advanced capabilities that help organizations:
- Increase the automation and accuracy of their decisions
- Generate faster, more competitive customer offers
- Unlock and grow into new customer segments
- Significantly reduce operational costs
Modern decisioning combines data and advanced analytics to generate highly accurate, automated decisions. Businesses can improve metrics like approval rates without increasing risk and, at the same time, provide customers with instant, personalized offers that grow competitive advantage.
In credit underwriting, for example, modern decision engines enable “better and more timely credit decisions and empower credit risk teams to operate with confidence in a more independent manner. In turn, better decisions lead to lower defaults and expand the market opportunity for lenders,” says Carlos Gonzalez-Cadenas at Index Ventures.
Not only do modern decision engines create a seamless decisioning process, but they can also be significantly cheaper and easier to integrate into a technology stack than traditional solutions – which tend to be much more complex.
How to choose the right decision engine for your business
Inspired by feedback from risk experts worldwide, here are the essential criteria you should consider when choosing a modern decision engine for your business:
1. Strong track record in your decisioning use case
The decision engine should have a strong track record in your current decisioning use case and the ability to power future ones.
At the same time, consider whether or not the provider has experience with companies in a similar stage of growth. In these instances, reviewing case studies or gaining insights from their existing users through reference calls can be helpful.
2. Ability to empower users through decision authoring
When choosing a decision engine, it is crucial that people across all levels of expertise – from your Data Scientists to the Head of Risk – feel comfortable using it. And your teams should be enabled to be as hands-on as they like when it comes to building and adjusting decision flows — from simple threshold setting to full workflow editing capabilities.
To be able to do this, the decision engine needs to have no-code and low-code capabilities that empower your non-technical users to easily use the platform and enable technical users to undertake more complex tasks.
3. Decisioning that supports all levels of sophistication
The decision engine should support the sophistication of your decisioning - now and as it grows.
Early-stage organizations must be able to build simple rule-based decision flows quickly and easily. And those with more advanced decisioning should be able to deploy statistical models directly on the platform or integrate models hosted on third-party platforms into their decisions.
4. Evidence-based testing and experimentation
Your organization must be able to monitor and improve its decisions in real-time based on data and insights gained through the decision engine.
It also helps if you can validate your decision flows using test data before you set them live. Plus, experiment with different versions of a decision flow on subsets of a customer segment.
5. Flexible, seamless data integration options
The decision engine should offer pre-built data integrations related to your geography, such as credit bureaus and open banking providers like Plaid or Codat, that integrate effortlessly into your decisions.
And if you have additional data needs, it should provide a custom integration solution.
6. Strong governance and collaboration structure
Having the proper guardrails in place will help your organization to make safer decisions. Therefore, the decision engine should allow you to set the permissions of various users depending on their role within your organization.
In addition, it needs to foster a collaborative environment – allowing users to work efficiently together to build, adjust, and approve decision flows.
7. Quick and straightforward technical implementation
Integrating the decision engine into your existing tool stack should be easy (now and in years to come), so you can start using the solution as soon as possible.
Additionally, the decision engine must support the technical requirements of your organization's use cases as they scale, especially when it comes to decision volume and latency.
8. Gold-standard data protection and security certifications
It is crucial that the decision engine caters to the data protection laws surrounding your product(s) and has a best-in-class data security certification such as ISO or SOC 2.
9. High reliability and ongoing support
Service-level agreements should provide extremely high reliability and uptime guarantees and offer your organization round-the-clock support.
10. Flexible pricing options that suit your organization
Pricing should suit the stage of growth and requirements of your organization. An important consideration is the upfront costs – as these may hinder you from being able to implement a new decisioning solution quickly.
This information provided in this article does not, and is not intended to constitute professional advice; instead, all information, content, and material are for general informational and educational purposes only. Accordingly, before taking any actions based upon such information, we encourage you to consult with the appropriate professionals.