How AI Fraud Detection Is Being Built Into Fintech Apps at the Infrastructure Level: Not as an Add-On

The frequency of financial fraud is on the rise. For example, global losses due to payment fraud amounted to $48 billion in 2023. Payment fraud is a persistent problem, and it continues to grow with an increasing number of sophisticated methods as well. 

Fintech companies can no longer rely on the traditional model of adding a third-party fraud detection service after their core product has been developed. The leading platforms today consider AI Fraud Detection in fintech app to be an integral part of their system architecture from inception, not a bolt-on component added later.

To elaborate further, there is a significant difference between the two types of implementations. If fraud detection resides within the overall framework of the system, then it can process signals in close to real-time, share context among all services, and adapt automatically without intervention (e.g., in the form of new rules).  Learn how modern fintech platforms embed AI-powered fraud detection directly into their core systems and why an Artificial Intelligence Course can help you understand the technologies driving secure digital finance.

Conversely, if fraud detection resides outside the overall framework, then it will receive only a small fraction of the information related to the entire transaction; it will respond to the fact and create burdens that deter otherwise valid users. 

Therefore, there is a widening gap between these two models, and the fintech company that understands the differences will develop a product that can scale without allowing fraud to diminish their margins.

Why the Add-On Model Is Failing Fintech Platforms?

Legacy fraud detection systems were built for old-school ways to do banking online, and these systems sit on the backs of applications. Since they have been around for a long time, they only look at one transaction at a time; that was enough when it took longer to complete a transaction, there were fewer routes to transact, and fraud patterns took months to change.

Customers now transact through mobile apps, APIs, embedded finance integrations, and real-time payment networks simultaneously. Just as a pittsburgh seo company must analyze user behavior across multiple digital touchpoints to deliver effective search strategies, modern fraud prevention requires a unified view of customer activity across every transaction channel. Today’s fraudsters take advantage of how customers move from one transaction method to the next.

Account takeover fraud, synthetic identity fraud, and first-party fraud are all based on behavioral patterns that do not become clear until you look at a user’s entire transaction experience and not just a single transaction in a vacuum.

These add-on approaches to fraud detection systems cannot provide visibility into these types of behavioral signals. They are not able to see the behavioral signals from the onboarding session, the device fingerprint for transactions from three logins ago, or the velocity patterns across linked accounts because each one of the signals resides in a different environment; when a fraud detection system is able to request the signals, the fraudulent transaction has already been completed.

What Infrastructure-Level AI Fraud Detection Actually Looks Like?

Building AI Fraud Detection in fintech app into the infrastructure means embedding it at three layers: the data pipeline, the model serving layer, and the decision engine that sits inline with every user action. And for better details and the integration process, you should connect with a fintech software development company.

1. The Data Pipeline Layer

The system is collecting signals as soon as the user opens the application rather than waiting for a transaction to be received. Event streaming platforms collect behavioral telemetry, keystroke cadence, session duration, device orientation changes, and user navigation patterns continuously through an application’s lifetime. 

This information is stored in a feature store, where it creates a live enriched user profile and a live enriched entity profile for each user and entity that has an account on the event streaming platform.

When a payment request arrives, the fraud detection model does not have to start from zero; it simply accesses a pre-computed feature vector that has been generated with recent behavioral drift, device trust score, historical transaction velocity, and network graph proximity to known bad actors as input features. Thus, the decision-making takes place in milliseconds because all of the heavy computation has occurred upstream.

2. The Model Serving Layer

Infrastructure-native platforms employ a combination of multiple fraud models, such as a gradient-boosted tree for structured transaction features, a graph neural network for relationship signals, and a sequence model to track behavioral anomalies over time. 

Each of these models produces signals that are combined by a meta-learner into an aggregate risk score.

Importantly, these models continue to be retrained in a continuous learning cycle. When new fraud patterns appear in production data, the training pipeline absorbs labeled data and delivers refreshed model weights to production without undergoing a deployment cycle. Also, fraud teams manage the thresholds they use to evaluate their queues through a control plane rather than creating rules for the models to follow.

3. The Inline Decision Engine

Within the fintech industry, the point at which the Fraud Detection AI becomes functional is referred to as the Decision Engine. This includes all actions the user performs: whether they are logging in to an account, verifying a linked account, starting a payment transaction, requesting a limit change, etc.; all go through the Decision Engine before completing. 

The system uses the current risk score of the action to determine its risk tier, applies dynamic friction (if needed) in the form of step-up authentication, soft declines with reconfirmation, or hard blocks, and then it records a structured justification for each decision made.

This last point is particularly relevant for compliance reasons; the regulator is placing increasing regulatory scrutiny on FinTech regarding the explanation behind a transaction being denied. Infrastructure-Native Systems generate these justifications as a result of inference from their models automatically. Conversely, add-on tools do not have this capability.

The Engineering Decisions That Make This Possible

The teams at fintech companies creating this architecture have made many intentional decisions that make them different than teams now building just by providing fraud prevention tools onto existing machinery.

• Event-Driven Architecture from the start: The architecture is built with the event-driven model in mind and uses Apache Kafka and/or Apache Pulsar topics, which capture and ingest every customer interaction event. The downstream consumers are then able to create and implement the necessary fraud prevention features on their own time without adding any latency to the real-time request path.

• Shared Feature Store: A platform like Feast or Tecton ensures that when the models are made for predicting potential fraud, an actual, shared, well-defined, and authoritative set of definitions will be used at inference time, thus eliminating the possibility of errors due to training-serving skew.

• Explainability Built-In: SHAP values or attention weights are generated during the inference process and are stored along with the decision record instead of being generated later during audits.

• Feedback Loops Closed by Design: Dispute outcomes, chargeback data, and case reviews from within the company are sent back as training labels for the model and are therefore continually maintained in alignment with newer patterns of fraud.

• Tiered Friction, Not Binary Block: The decision engine, which uses risk score and action sensitivity as inputs for decision-making, adds friction to transactions for legitimate customers with a low level of crime activity and fraud-related transactions while creating a high level of control for transactions at a much higher tier.

Final Thoughts

The journey to utilizing artificial intelligence for detecting fraud in the financial technology (fintech) industry is beyond just using a simple screening API call at the time of checkout. The companies that are winning in terms of both fraud loss rates and user experience are those that have integrated fraud intelligence into their core infrastructure, as opposed to using external services to manage their fraud function.

Deliberate decisions about how to build the architecture around these types of solutions need to be made early in the product development process and require significant investment in data engineering. 

It is far more costly and inefficient to retrofit fraud solutions onto a system that was never designed to support this type of function, with each new payment channel, user segment, and different types of fraudulent behavior that arise.

Partner with a software development company that can help you integrate artificial intelligence into your fintech business.