How to Combat Fraud in FinTech: A Guide to Machine Learning and the IDEAS Framework
Key Lessons from 6 years of real-world experience to fight fraud while scaling growth
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In the early days of working at a fast-growing FinTech startup, we hit a major roadblock: fraud losses.
Growth numbers looked great, but we struggled to keep up with risk behind the scenes. Customers weren’t paying and we soon realized they were committing fraud.
A fraud ring had targeted us, knowing our early-stage product lacked sophisticated defenses.
By the time we identified the problem, we had already lost thousands of dollars—a devastating blow for a small but growing startup.
The Real Costs of Fraud
Fraud costs more than money. It can:
Erode trust: Customers, payment processors, and investors lose confidence.
Drain resources: Fraud investigations consume valuable time and resources on the lean startup team.
Disrupt operations: Teams are forced to shift focus from growth to firefighting.
Demoralize employees: Constantly responding to fraud is exhausting!
Introduce legal risks: It’s stressful and time-consuming to deal with scrutiny and potential fines from regulators.
Ripple effects from fraud can have dire consequences - from jeopardizing fundraising to damaging reputations and slowing product development.
For startups, this can spell disaster. But here’s the good news: we found a solution with AI.
Why Machine Learning Is Key to Fighting Fraud
When we first encountered fraud, our defenses consisted of simple “if-then” rules, such as rejecting signups with duplicate phone numbers.
This worked… until it didn’t.
Fraudsters adapt quickly, and manual rules are slow to keep up.
Some limitations of rules-based systems I’ve experienced were:
Rigidity: Static rules can’t evolve with fraud tactics.
High false positives: Rules can gradually block more and more legitimate users over time as fraudsters iterate and use new methods.
Time-consuming: Analyzing and deploying new rules takes hours or days depending on the system.
Operational complexity: Systems with dozens of rules become complicated and unmanageable over time.
How Machine Learning Transforms Fraud Detection
Machine learning (ML) was the turning point in our fight against fraud.
Our ability to detect fraud progressed from a reactive, rule-based approach to a proactive, adaptive system.
Because ML models continuously learn from new data, allowing for:
Proactive detection: Identify emerging fraud patterns in real-time.
Flexibility: Adapt to changes in fraud tactics and customer behaviors.
Efficiency: Automate fraud detection, freeing up team resources for more impactful development work.
Going from deterministic rules to probabilistic models requires a mindset change, but the rewards are worth it.
The IDEAS Framework for Applying ML
If you’re exploring ML for fraud detection, consider this framework:
Identify business area: Hone in on an area of the business that would benefit most from automation.
Define key metrics: Focus on metrics that directly impact the bottom line, such as first-payment default rate or fraud rate.
Evaluate data sources: Assess whether your data captures essential information (e.g. user behaviours, historical payment patterns) that are predictive of fraud.
Ask predictive question: What specific problem are you solving? Formulate this into a predictive modeling question.
Survey potential solution: Consider which ML algorithms best align with your dataset and the problem you're solving.
Applying the IDEAS Framework: Tackling Loan Fraud with ML
In one case, we noticed an uptick in loan defaults for existing customers.
Fraudsters were repaying initial loans to qualify for higher-limit loans, only to default later.
These loans were 3x-10x larger than initial loans, significantly amplifying losses and quickly impacting our portfolio’s bottom line.
To address this, I worked with a cross-functional team and applied the IDEAS framework:
Identify business area: Higher-than-expected defaults on loans for existing customers with longer tenures.
Define key metrics: Focus on first payment default rates of repeat loans.
Evaluate data sources: Audit available data to use as features or inputs to the model.
Ask predictive question: "What is the likelihood of an existing customer defaulting on a subsequent loan?"
Survey potential solution: Build a gradient boosted model to predict fraud in real-time for existing customers.
The resulting model performed well, reducing default rates by 30% and made a meaningful business impact.
This solution reduced losses and allowed the portfolio to scale healthily.
Conclusion
Fraud can cripple a startup’s growth, but with the right tools, you can stay ahead.
Machine learning offers the flexibility and scalability needed to outpace fraudsters. My IDEAS framework provides a practical roadmap to implement it.
If you're tackling fraud at your startup, start by evaluating where ML can make the biggest impact and take action.
The right approach to fraud detection not only protects your business but fuels growth and sets you up for long-term success.
Note: AI Weekender has moved. New posts are published at ai-weekender.com, and this Substack is now an archive.
To keep receiving weekly issues, please subscribe at ai-weekender.com instead of here.
Great Post! Seems like fraud tactics are always evolving, does this bring upon the importance of inference to understand how fraud is changing?