Dilemma of fintechs and compliance

Is there a compromise between minimal oversight and full compliance systems?

Building out a full compliance management system from scratch would present a significant challenge for fintechs, especially in the early days. Established banks have developed expertise over decades, which require significant capital to deploy and maintain. Now that funding for new fintechs seems to be coming back, is there a way to support that innovation without overburdening their teams and resources?

Clearly, the era of doing little and having minimal oversight capabilities over these programs didn't lead us to the promised land but neither would the other end of the spectrum. What would be the common sense approach then and how could software help avoid manual work especially in the testing and monitoring and risk assessment areas? We think there's a practical alternative – fintechs can and should create a more efficient, data-driven approach to compliance monitoring.

The Limitations of Traditional Compliance for Fintechs

Let's examine the major tenets of a robust compliance management system first. The conventional approach to compliance typically involves:

Developing comprehensive written policies and procedures
Running employee training
Implementing control testing and monitoring programs
Conducting periodic risk assessments

On top of that, compliance teams tend to maintain extensive documentation and evidence files, as well as establish a multi-layered governance and reporting structures. For most fintechs, this approach presents several practical challenges:

Constrained resources: Traditional compliance programs require dedicated teams and significant time investments from stakeholders across the organization. Most fintechs simply don't have the luxury of assigning multiple full-time employees solely to compliance activities.
Static assessment cycles: Conventional risk assessments provide only periodic snapshots—typically quarterly or annually. In the environment where change management is paramount, these assessments quickly become outdated.
Subjective evaluation methods: Traditional assessments rely heavily on qualitative judgments and self-reporting. Business units evaluate their own risks and control effectiveness using subjective scales that can vary widely depending on who completes the assessment.
Limited operational visibility: Perhaps most importantly, conventional approaches often lack direct insight into how products actually function in real-world conditions. They assess what should happen according to documented procedures, not necessarily what does happen when customers interact with your systems.

Instead of investing scarce resources in replicating traditional compliance management systems, fintechs can take advantage of their technical competence to build a more effective approach through data aggregation and automated reasoning.

The core concept is straightforward but powerful: collect comprehensive runtime data about your product's actual operations, then evaluate this data against regulatory requirements in near real-time.

An Alternative

Key components of a data-driven compliance system include:

Comprehensive Data Collection: Rather than relying on manual assessments, capture detailed data on all relevant aspects of your product's behavior — end user actions, front-end events, transaction flows, decision points, disclosures, calculations, and more — creating a complete digital trace of how your product truly operates. Process this data through an ontological layer that readies it for analysis.
Regulatory Rules Engine: Develop or implement a rules framework that translates regulatory requirements into testable conditions. These rules can evaluate whether specific actions, timings, disclosures, or calculations meet applicable standards.
Continuous Automated Analysis: Instead of point-in-time assessments, continuously analyze operational data against regulatory rules to identify potential compliance issues as they occur rather than discovering them months later.
Real-Time Compliance Dashboard: Provide compliance teams with dynamic visualizations showing compliance status across products and regulatory requirements, highlighting exceptions and trends without requiring manual data gathering.
Evidence Repository: Automatically maintain a structured collection of evidence demonstrating compliance with key regulatory requirements, readily available for internal reviews or examiner requests.

This approach offers several meaningful benefits, but most importantly I would argue that it flips the traditional approach to compliance and risk management on its head by reducing the need and reliance on traditional, less effective risk assessment exercises and instead providing you with a machine that determines the state of your compliance at any point in time. There's other benefits of course that we should discuss as well:

Automation: Rather than building a large compliance department to perform manual assessments, fintechs can leverage technology to automate much of the monitoring process. A small compliance team can oversee a much larger operation by focusing on exceptions and issues identified by the system.
Accuracy: By analyzing actual operational data rather than relying on manual compliance self-assessments, this approach provides a more accurate picture of compliance status. There's less room for subjective interpretation when you're examining concrete data on system behavior and customer interactions.
Proactive Issue Identification: Instead of waiting for scheduled risk assessments or audit findings, compliance issues can be identified as they emerge—often before they affect large numbers of customers. This enables faster remediation and reduces regulatory exposure.
Scalability: As the fintech grows and adds new products or features, the compliance monitoring framework can scale accordingly without requiring proportional growth in compliance staff. New data sources can be integrated, and additional regulatory rules can be added to the analysis engine.
Defensible Documentation: When regulators examine your operation, you can provide concrete, data-backed evidence of compliance rather than just policies and attestations. This objective documentation can help build credibility with examiners and demonstrate a serious commitment to compliance.

One of the most valuable aspects of this data-driven approach is how it transforms the risk assessment process. Instead of relying on subjective evaluations completed in workshops or spreadsheets, compliance officers can directly observe where compliance issues are occurring and which controls are functioning effectively.

For example, rather than subjectively rating "risk of UDAAP violations in marketing materials," the system can show exactly how many customers viewed each disclosure, how long they spent reviewing it, and whether the disclosure was presented before or after key decision points. This concrete data provides a much clearer picture of compliance risk than subjective ratings.

The result is a risk assessment that's:

Evidence-based rather than opinion-driven
Continuous rather than periodic
Specific rather than general
Resource-efficient rather than labor-intensive

Implementation Strategy

While this approach offers significant advantages, implementing it effectively requires some thought.

Prioritize Your Regulatory Requirements

Begin by identifying which regulations present the highest risk to your business model. Don't boil the ocean with the alphabet soup of regulations, focus your efforts on monitoring compliance with these key requirements that apply to your actual product.

Identify Essential Data Points

Determine what operational data you need to capture to demonstrate compliance with each priority requirement. This might include transaction details, user interactions, timing of disclosures, fee calculations, and communication records. This is where the ontology plays a critical role, making this data useful.

Implement Strategic Logging

Ensure your systems record all compliance-relevant actions and decisions. This may require enhancing existing logging capabilities or adding new instrumentation to your applications.

Develop Analytics Capabilities

Build the capacity to analyze logged data against regulatory requirements. This could range from simple rule-based checks to more sophisticated pattern recognition.

Create Self-serve Dashboards

Design visualizations that translate complex compliance data into actionable insights for compliance and back office teams without requiring specialized technical knowledge to interpret.

Maintain Expert Oversight

While much of the monitoring can be automated, experienced compliance professionals remain essential to interpret results, investigate exceptions, and make nuanced compliance judgments that require human intervention.

Transformation in Practice

Consider a digital lending fintech that previously conducted quarterly compliance risk assessments using traditional methods. The process required multiple team members to spend weeks gathering information, interviewing stakeholders, and documenting findings—yet still provided limited visibility into actual operations.

After implementing a data-driven compliance approach, the same company now:

Continuously monitors all loan applications, approvals, and servicing activities
Automatically verifies disclosures against regulatory timing and content requirements
Analyzes pricing decisions for potential fair lending disparities in real-time
Provides compliance officers with daily dashboards showing key compliance metrics

The result is a more effective compliance program that requires fewer resources. The compliance team spends less time on documentation and more time addressing actual issues. The company identifies potential problems faster, often before they affect significant numbers of customers. And when regulators examine their operations, they can provide concrete evidence of compliance rather than just attestations.

Conclusion

For fintechs, attempting to implement traditional compliance management systems often means diverting limited resources to processes that weren't designed for digital-first organizations. But I bet they could achieve better compliance outcomes with fewer resources. It doesn't mean however that you eliminate compliance expertise with data altogether. In fact, it makes that expertise more valuable by focusing it on analysis and remediation rather than documentation.

As regulators increasingly focus on actual consumer outcomes rather than just policies and procedures, this evidence-based approach to compliance positions fintechs well for sponsor bank oversight and any regulatory scrutiny. Providing compliance teams with comprehensive visibility into product operations through reliable data analysis is essential for fintechs to create this more effective and efficient alternative to traditional compliance management.