3 Tips for Using Tech to Sift Out FinTech Risks
FEATURED POST
Fintech is one of the most promising and rapidly growing industries out there today.
According to data from Grand View Research, its global market value was worth over $266.56 billion in 2022. The industry is growing at a healthy 17.5% CAGR, which predicts a whopping $949.49 billion market value by 2030.
So, while fintech has never moved faster, that speed also comes with risks that traditional systems were never built to handle.
Every transaction, API call, and data transfer introduces potential exposure, and the complexity only grows as operations scale.
Today, let’s look at three ways we can use technology not as a shield after things go wrong, but as a preventive filter of risk.
Build a Layered Risk Radar
Relying on a single model or detection rule to manage risk is like guarding a vault with one lock.
A stronger approach uses multiple lenses: predictive analytics, anomaly detection, and scenario testing. Each method catches something the others miss. Machine learning can find behavioral irregularities, while stress simulations show how systems react under pressure.
Calibration matters as much as sophistication.
Without constant retraining, even high-performing models lose accuracy over time. Fintechs can maintain sharpness by injecting synthetic anomalies or “stress data” to check if the system still responds as expected.
Research out of China had interesting insights to learn from in this regard. They found that hybrid models using MS-VAR and ARIMA improved early risk detection by more than 20% compared to traditional methods. These are time-series models used for forecasting and analyzing data that change over time.
MS-VAR and ARIMA are both types of time-series models used for forecasting and analyzing data that change over time. These include factors like stock prices, credit risk levels, or market volatility.
With this tech, you are using a layered view where each layer learns from the other instead of working in isolation.
Secure Mixed Domain Risks
Even the most sophisticated fintech systems can’t predict every threat.
This isn’t a weakness that’s limited to fintech companies. Legal and reputational risks often appear from far outside a company’s technical boundaries. Just look at the ethylene oxide lawsuit (EtO) situation. Even though it’s not a fintech situation, it offers a great parallel on how risk can arise from unexpected areas.
Steregenics is a medical device sterilization company that ended up getting stuck in legal drama because EtO exposure ended up affecting people. According to TruLaw, the company has already had to pay out $771 million! The company would never have expected that chemical exposure would end up being their risk.
Most risk analysts would have assumed that improper sterilization or quality control would be the main risk factors to worry about. These unexpected angles of risk can happen in any industry, including fintech.
A relevant example would be the Facebook-Cambridge Analytica scandal.
It was built to connect people and monetize ads, but third-party misuse of data made its technical business model a global ethics and privacy crisis. Though news broke in 2019, it was only in July, earlier this year, that Mark Zuckerberg settled the case, paying $8 billion.
This is why you have to look at every area an institution intersects. Fintech companies should be paying close attention and learn from cross-domain exposure cases in any industry.
The key takeaway here is that a system designed to manage only financial risk may fail to recognize broader risk patterns.
Continuous Monitoring of Your Tech Stack
Once a fintech’s systems are up and running, it’s tempting to let them run quietly in the background.
However, that’s where trouble often starts. The same technology built to detect risk can begin introducing new kinds of it if left unchecked. This is why continuous monitoring and validation keep those blind spots from multiplying.
Interestingly, this is one area where AI can be of help.
We already know from reports by McKinsey & Company that generative AI is going to be critical in helping banks deal with risk and compliance. They are recommending banks to start looking at 3-5 high-priority use cases as Gen AI is set to transform banking over the next 5 years.
This means watching for model drift, tracking data integrity, and ensuring feedback loops remain active. Teams should also perform “red team” tests—purposefully challenging systems to see where they fail. A growing number of firms now automate drift detection, retraining models when accuracy drops below a certain threshold.
Vendor risk needs equal attention.
APIs, data providers, and outsourced partners can all become weak points if not monitored properly. A simple dashboard that flags potential supplier issues can save a fintech from reputational and financial fallout later.
Frequently Asked Questions
1. What is risk in fintech?
Risk in fintech means the chance that something could go wrong with digital financial operations, like fraud, data breaches, or regulatory issues. Since fintech blends tech and finance, risks often overlap between cybersecurity, compliance, and system reliability.
2. What are the 4 types of risk in finance?
The main types are market risk, credit risk, liquidity risk, and operational risk. Market risk comes from price changes, credit risk from borrowers not paying back, liquidity risk from cash shortages, and operational risk from internal failures or external disruptions.
3. How is AI used for risk management?
AI helps spot unusual patterns and predict potential issues before they blow up. It’s used for fraud detection, credit scoring, and monitoring transactions in real time. By analyzing huge amounts of data quickly, AI helps financial teams make faster, smarter risk decisions.
It’s clear that technology has transformed risk management from a static compliance exercise into a living, intelligent process.
Predictive models are now able to reveal patterns before they become losses, and AI tools are ensuring that continuous monitoring systems remain trustworthy over time.
Fintech leaders who invest in these layers are building a foundation of agility that will allow innovation to continue safely.
In the coming years, we’ll likely see that the strongest companies will be those that use technology to eliminate risk before it ever makes a sound.