Who determines loan approvals, credit ceilings, and portfolio exposure today? For most modern businesses, the answer is a credit engine — an automated system that interprets data and executes high-stakes financial decisions at scale.
But as every business operates with their own customer profiles, risk appetites, and market conditions, no single credit-decisioning system will work the same way for everyone. This is why many companies eventually find themselves at a crossroads: unsure whether to build best-fit credit decisioning systems or buy one for a quicker path to market.
This article will help you understand the trade-offs behind control, cost, and speed to finally get your credit engine going instead of pondering endlessly over the ‘best’ decision (because spoiler alert, there isn’t one).
Conditions that influence your credit engine decision
At its core, buying or building comes down to a few questions:
- What level of control do you need?
- How fast do you need to go to market?
- How much internal talent do you actually have?
Level of control
If your business is expected to evolve rapidly or deals with volatile customer behaviour, ownership of your credit engine will grant greater flexibility for credit policy and data logic tweaking.
With buying, you sacrifice some of that granular control in exchange for tried-and-trusted frameworks – so it really depends on your confidence that your business won’t dramatically change down the road.
Go to market speed
Buying can dramatically shorten that timeline with pre-approved know your customer (KYC) tools. That said, the quick turnover may come back to bite in the form of customer dissatisfaction if abrupt changes are made later by the platform without your inputs.
Depth of internal talent
Buying or building isn’t the end. Credit engines need ongoing performance monitoring as business needs evolve, and compliance updates as regulations change. Choosing to build from scratch means that your team will have to handle any troubleshooting or alterations later.
That’s not to say that buying eliminates the need for internal expertise, but providers will likely have seasoned support teams ready to assist you.
When building a credit engine makes sense
Building your own credit engine may make more sense if:
- You deal with unconventional customers
- You need freedom to test new credit models
- You are confident of achieving economics of scale
Processing real-time data
If industry averages feel outdated to you by the time you get them, building your own credit engine may help process signals such as platform engagement signals, transactional metadata, and fulfillment patterns faster. And by faster, we mean in real time.
Freedom to experiment
Off-the-shelf templates may be too rigid for testing alternative credit-scoring logic, automate policy updates, or run A/B experiments on risk thresholds. Instead of giving up the initiative and waiting on provider roadmaps, building your own credit engine can grant your team the ability to adapt to conditions as they surface.
Of course, your team has to be capable enough to handle experiments alongside massive data volumes to fully maximise this flexibility.
Achieving economics of scale
Though building may be slower and more costly in the beginning, it can pay off significantly once you reach a certain scale. This is because cost per evaluation drops sharply once decision volumes surpass critical pass.
Full ownership over a credit engine means expansion into new markets or product lines can be done with less artificial restrictions that providers may have in place.
If you anticipate a future where proprietary Machine Learning models will be pivotal to your competitive edge, this is possibly one of the strongest reasons why a self-built credit engine would be the way to go.
When buying a credit engine makes sense
Buying a credit engine from a provider may make more sense if:
- You value speed and stability over customisation
- You wish to lower operational risk
- You have limited resources to divert to credit engine building
Speed and stability
Ready-made credit engines give you production-tested scoring modules, KYC/KYB automation, plus built-in reporting and compliance tools straight out of the box. If credit forms just one portion of your offerings, buying may be the way to go.
Minimise operational risk
Buying also brings relief to operational load, a critical factor for lean teams who are unlikely to withstand added pressure from tasks such as model maintenance, rule updates, infrastructure scaling, and audit-ready documentation.
Resource optimisation
Business leaders will likely be happy to hear that buying keeps things moving by granting you a tried-and-tested roadmap from the provider so that you can continue allocating precious resources towards maintaining credit pipelines, product development, and growth.
Modern credit engines also offer modular APIs, allowing you to spend only on required components instead of being locked into full-stack builds, at least until your business matures further.
Choose the partner that makes sense
Whether you build or buy, the real work always comes after credit engines have been set up. Choosing the right partner can make all the difference.
Offering both granular, customisable foundations for teams that want control as well as plug-and-play APIs for those who need stability and fast deployment, Bettr ensures that your credit engine runs smoother both during launch and beyond.
Our AI-driven Credit Engine enables faster and more efficient credit assessment and decisioning, reducing the time needed to deploy credit policies from weeks to minutes. Partners can simply configure their own policies by dragging standardised strategy modules from built-in template library, covering key components such as underwriting rules and drop strategies, all within the code-free interface.
Combined with rapid strategy testing and comprehensive performance monitoring, the credit engine allows partners to respond quickly to the evolving needs of MSMEs.
Bettr’s strength lies in its Alternative Data Intelligence which transforms sparse and local data sources into reliable credit attributes. By applying advanced AI, we help partners to build comprehensive and precise risk profiles for MSMEs and power truly inclusive financing at scale.
