Descubre el futuro de las finanzas en América Latina y el Caribe

The future of finance in LatAm & the Caribbean

O futuro das finanças na América Latina e no Caribe

The Ultimate Guide to Decision Engines

3 October, 2022

Ivis Aguilera

Sponsored by Provenir

Decision engines are software platforms that automate business rules or decisions – helping you streamline processes, centralizing the decision-making process and saving you from manual work. But what does a decision engine need to run? Besides the set of rules (logic), otherwise known as the decisioning workflow, decision engines need data. By accessing and integrating data from multiple sources and applying these ‘rules’ according to your criteria, you can automate decision-making. In the finance world, decision engines are used to help you determine who to lend to and which sorts of products you can offer. Decision engines can also enable personalized pricing and offers (i.e., finance terms and interest rates), all of which are customizable to your unique needs. Examples in the world of fintech/financial services include: consumer lending, loan origination, credit card approvals, auto financing, point of sale lending like buy now, pay later (BNPL), lending to SMEs, insurance policy approvals, upsell/cross-sell offers, champion/challenger strategies, audits, collections and more. There is immense value in using decision engines in financial services instead of manually making decisions, including improved performance, profits, efficiency, flexibility, scalability, consistency, and transparency.


Decision Engine Framework

While it’s up to each individual organization how they want their decisions to be executed, there are some basic steps that remain true across the board.

  1. Set Desired Outcomes: Look at what your goals are. What are the specific business rules that you need your decision engine or workflows to execute on?
  2. Determine Decision Criteria: What are the standards or requirements to which you are making your evaluations or decisions? For example, in the case of many credit applications, particular criteria often include income, job status, age, marital status, debt ratio, etc.
  3. Organize Data Sources: To process these business decisions based on your desired outcomes and your determined criteria, what sort of data sources do you need? Do you need traditional credit bureau data, third-party sources, alternative data like rental info, social media presence and web data, etc.?
  4. Create Decisioning Workflows: What are the necessary steps in your decisioning process? Use the configuration tools within your decision engine to lay out your workflows and business rules and enable automated decisions.
  5. Test and Iterate: Create, test, and deploy your modelling scorecards and decisioning process, and look at what happens when a typical customer is put into your system.
  6. Determine Next Steps: Where is your threshold for complex applications? Which applications need manual intervention? Straight-through processing enables instant decisions for more simple credit and lending requests, while a rules-driven decisioning process helps to identify and re-route exceptions that require more manual intervention.
  7. Monitor and Optimize: Is your decision engine offering real business value? Keep tabs on your decisioning performance by using the information your decision engine gives you. Identify opportunities for further enhancement of your decisioning process and tools and enable more efficient decisioning – and business growth.

Think of all the manual decisions that require human intervention. If an individual needs a car loan, how does a lender determine if that individual is creditworthy? Or which interest rate or repayment terms should be offered? Having an automated decision engine can streamline the application, approval, and funding process to ensure an efficient, superior customer experience. This also allows you to focus that most precious resource, humans, on the more complex cases that require manual intervention.


Data, Data, and More Data

There can be no automating decisions without extensive data – varied, and from a wide range of sources. All financial services organizations use data to make informed decisions across the customer lifecycle but having to manually access and integrate data sources can be challenging.

These days, more lenders are looking to a wider range of sources, including alternative data like rental payments, social media interactions, website info, travel data and more, to ensure:

  • A more accurate view of identity verification
  • A more holistic view of risk and creditworthiness
  • Better fraud prevention

All this data must be accessed, analyzed, and actioned appropriately to help ensure more accurate decisions. Having a variety of data available on-demand is essential for enhancing your decisioning. Third-party data providers, connected through a centralized platform or marketplace with a single API, can make this data consumption effortless, giving you the ability to access and integrate numerous data sources in minutes. Use that data to test your decisioning workflows, and then iterate and adapt with ease.

Want more info on how to make smarter, faster decisions with real-time data? Check out our eBook.

Jose Vargas is General Manager and Executive Vice President, LatAm, at Provenir, which helps fintechs and financial services providers make smarter decisions faster with its AI-Powered Risk Decisioning Platform. Provenir works with disruptive financial services organizations in more than 50 countries and processes more than 3 billion transactions annually.