Fraud Prevention: What Fintechs Really Need

I love my job because I get to speak to great companies that all have certain things in common: they have software built into their core products, they are great connectors of people and services, they earn the trust of their customers and re-earn it in competitive environments. Fintechs have a knack for finding the balance of risk and growth, whether it’s looking at potential for fraud, credit risks or other areas.

But each company is unique in how it manages fraud and risk. Their organization structures are different. Some have Risk Engineering organizations while others don’t distinguish Risk Engineers from other Software Engineers. Risk organizations can separate teams based on fraud type, transaction type, or business unit. Some have large data science teams dedicated to fraud detection and prevention, others have to “borrow” data science teams that focus on other areas like marketing or financial modeling.

Some fintech companies focus more on credit or applications while others focus on transactions. Some offer one core product while others offer several. Some service merchants on the payments/acquirer side while others focus on issuers and payers.

So when a fintech person asks me – “What do you think we need?” – my initial response could be unsatisfying: “it depends, let’s talk about your business.” In this blog, however, I explore some core tenets to guide any fintech company.

Fraud Runs Rampant in the Financial Industry

Whether it’s identity theft, payment fraud, or elaborate scams, bad actors are constantly finding new ways to exploit vulnerabilities in the fintech industry.

Take digital identity and synthetic identity fraud, for example. Fraudsters are getting smarter, using stolen or fake identities to sneak past weak identity verification processes, especially during onboarding. That’s why solid Know Your Customer (KYC) protocols are so important. Smart tools like artificial intelligence, behavioral analytics and document verification can help confirm users are who they say they are, without slowing down the process.

Then there’s payment fraud, which keeps evolving. From fake ACH transactions to stolen credit card info, fraudsters know where to hit. Fintechs need real-time tools that don’t just block fraud after it happens but catch unusual behavior before things escalate. The goal is to protect customers and businesses without disruptions or unnecessary roadblocks.

At the end of the day, trust is everything in the fintech industry. People need to know their money and personal data are safe. By focusing on smarter fraud prevention — whether it’s tightening up identity verification, boosting onboarding security, or spotting scams like phishing or money laundering before they happen — fintechs and financial institutions can stay ahead of the curve and keep building confidence in this fast-moving, digital-first world.

Safeguarding Customer Data

With the rise of bots and other automated threats, fintechs face increasing risks of unauthorized access to sensitive information and unauthorized transactions. These threats often thwart risk management measures and exploit vulnerabilities in systems, enabling criminals to steal customer data like phone numbers, social security numbers, and other sensitive information.

To stay ahead, fintechs are turning to automation as a powerful tool for identifying and addressing suspicious activity in real time. Automated systems can monitor vast amounts of data, flag anomalies, and respond to threats faster than manual processes ever could, helping to prevent breaches and mitigating the potential for fraud to escalate.

By combining smart automation with robust security measures, you can maintain the integrity of your systems and safeguard customers’ data — and their trust in your company.

Data Is Your Friend

Risk and fraud is a data game – but legacy fraud prevention tools are hard to work with. Data integration has become a scary concept fraught with delays, headaches and expensive professional services.

A true data orchestration tool that can consume a variety of internal and third-party data sources is a critical foundation to fraud prevention. Even if you have a great model built by world-class data scientists, it won’t get you far if you’re not working with the right data.

In the past, simply submitting authenticated transactions for decisioning was often sufficient to ensure security. That’s no longer the case. Today, leveraging third-party data sources is essential for validating identities. Device intelligence plays a critical role in monitoring account takeover (ATO) attempts, while data on login events is also crucial for identifying potential fraud risks.

Silos Are Your Enemy

One of our potential customers had a great model for ACH transactions, but was still experiencing a lot of fraud. Why? Because they suffered from ATO attacks, and their customers’ credit cards were compromised. This highlights a critical point: fraudsters, like water, will always seek the path of least resistance to achieve the greatest reward. Even if you’re highly effective at preventing fraud in one area, those efforts can be undermined if other areas of your system remain exposed. A holistic approach to fraud prevention is essential to closing these gaps.

Device Data Is Table Stakes

Many companies wait until ATOs become a serious problem before investing in device intelligence; but by then, the damage is done. The reality is, device intelligence isn’t just a nice-to-have – it’s table stakes for preventing modern fraud. Without it, you’re leaving gaps in your defenses that fraudsters are eager to exploit.

Relying on separate solutions for iOS, Android, and web introduces inconsistencies in your signals and unnecessarily complicates your teams’ workflows. A unified device intelligence solution that covers all platforms – JavaScript, iOS, and Android – simplifies implementation and ensures you’re getting consistent, actionable data across your entire ecosystem. With the right solution, you can run a proof of concept (POC) in as little as two weeks and start closing those gaps.

If you’re not using a consistent device ID to track users across your mobile app and website, or collecting robust data attributes about their behavior, you’re leaving the door open for ATO fraud. And, once an ATO occurs, it’s only a matter of time before losses escalate into other areas. Staying ahead of fraud means adopting comprehensive, proactive solutions now – not waiting for a costly breach to force your hand.

Every SML Model is Stale – or Will Be

Data scientists are some of the smartest people you’ll meet. (We’re lucky to have several brilliant minds here at DataVisor.) But even the most honest data scientist will tell you that no one beats Father Time. The methods that work today might not be as effective six months or a year from now, especially as fraudsters constantly evolve their tactics.

It doesn’t matter how strong your data science team is, whether in-house or contracted. They could be world-class experts like DataVisor’s own Harvard-trained Ryan Duncan, but the truth remains: fraud evolves faster than most can react. By the time you’ve identified and adapted to new fraud patterns, you’ve likely already faced financial losses, reputational damage, and other costly consequences.

Fraud is also no longer a one-off event. Coordinated fraud rings launch sophisticated attacks that don’t just target individual transactions but exploit weaknesses on a larger scale. Investigating these threats is time-consuming and often reactive, which gives attackers the upper hand.

This is where unsupervised machine learning (UML) disrupts the status quo. Unlike supervised models that rely on historical data or analyze transactions one by one, UML looks at behavior in aggregate, identifying patterns and anomalies that would otherwise go unnoticed. It’s your ultimate backup because it relies on AI, not people, and works tirelessly to identify fraud patterns, provide predictive analytics and outsmart fraudsters. While fraudsters have embraced AI to get ahead, as a fraud fighter in the fintech sector, you must do the same.

Every Model Has an ROI

How do you measure success in financial fraud prevention? It’s not just about reducing the number of fraudulent transactions or keeping false positives in check. True success lies in evaluating the overall return on investment (ROI), factoring in the dollars saved from fraud prevention, the cost of false positives, and the impact of friction on the user experience.

The goal isn’t 100% prevention of financial crime – achieving that often means sacrificing revenue and adding unnecessary friction to your customer journey. Instead, smart fintech fraud prevention focuses on optimizing where and how to intervene. It’s about accurately distinguishing true fraud from false positives while ensuring a seamless experience for legitimate customers.

These metrics are measurable and actionable. With the right tools and insights, you can calculate your $ ROI, providing a clear picture of the value your fraud prevention strategy brings. And let’s face it: ROI is what resonates with leadership, making it a key driver in demonstrating the success of your efforts.

Customer Journeys Aren’t Just for Marketing

Reducing friction isn’t just a security concern; it’s also a significant opportunity for your Marketing and Product teams. These teams focus on driving conversions, growing assets under management (AUM), and expanding the customer base. What’s often overlooked is that the customer journey is just as relevant to fraud prevention as it is to marketing and lead conversion.

To maintain a seamless customer experience, friction needs to be applied selectively and strategically.

• No blanket measures.
• Categorize your customers by risk level, which can often be done without requiring steps from users.
• Target high-risk users with custom sequences of verification steps that you can test and iterate.

This approach ensures that legitimate customers enjoy a smooth journey, while high-risk interactions are flagged and managed appropriately. It also can improve the ROI on marketing efforts by better calibrating when to call out to 3rd party data signals and ensure that marketing conversions don’t lead to fraud losses.

Flexibility is key here. You need the ability to define risk thresholds, tailor interventions based on evolving insights, and iterate as fraud tactics and customer behaviors change. This balance – protecting against fraud while preserving a frictionless experience for most users – strengthens customer trust and supports business growth.

Integration Isn’t Orchestration

I’ve already touched on the importance of data and the need to overcome data silos, but let’s take it a step further. The term “orchestration” often gets used interchangeably with “integration,” but there’s a distinct difference. Integration is about connecting to a given data source. Orchestration, on the other hand, is about how and when you leverage those connections to make smarter decisions.

Integrations are essential, especially given the wealth of third-party data signals available to enhance your decision-making. At DataVisor, we encourage you to test those signals, build redundancies, and strategically call on them during key points in the customer journey. The key here is the “when” – calling out to data sources only when necessary.

This selective approach is what data orchestration is all about. Pulling data from six different sources for every transaction is costly and inefficient. Orchestration means dynamically tapping into third-party signals based on the risk profile you’ve determined during the customer journey. It gives you the flexibility to test and incorporate signals as needed, ensuring a more cost-effective and impactful strategy.

True Prevention Plays Well with Others

Some view fraud prevention technology as a standalone category, separate from the tools engineers use daily, but that approach doesn’t hold up. Your fraud prevention solution should integrate seamlessly with your existing infrastructure, deploying on the cloud platform of your choice.

For example, if you’re an AWS shop, your solution should work in AWS. Likewise, the data within your fraud system should remain portable, allowing you to store and manage it in the data infrastructure you prefer.

If you use Snowflake as a data lake, older fraud data should be easily transferable there, and relevant data for risk assessment should flow back into your decision engine when needed. For fraud investigations, APIs should connect seamlessly to systems of record like ERP tools or customer-facing platforms like Salesforce, keeping everyone aligned on the status of a customer’s transaction.

Fraud prevention must also work with the communication tools your teams rely on, like Outlook, Gmail, or Slack, enabling fraud alerts to be orchestrated without requiring direct access to the fraud tool’s UI. The goal is clear: systems that communicate effectively and teams that work in sync. One team, one dream.

Putting It All Together

Risk management isn’t just about preventing losses but enabling better customer experiences, growing your product’s adoption and facilitating healthy growth. As your team builds and strengthens your risk management system, use the following checklists of essential features and capabilities to ensure your fintech organization is future-proofing fraud prevention.

7 Essential Capabilities & Features Checklist

• Data Orchestration
  A unified system capable of integrating various internal and third-party data sources in real-time, including device intelligence across multiple platforms
• Unsupervised Machine Learning (UML)
  Employ UML to complement traditional rules and supervised machine learning models. UML is critical for identifying sophisticated fraud rings and attacks.
• Accessible Analytics
  Integrate analytics directly within your risk platform to eliminate complex integration builds, enabling easy access to insights that demonstrate ROI, identify trends, and inform iterative strategies in an evolving fraud landscape.
• Identity Verification
  Implementation of robust KYC protocols and AI-powered tools for comprehensive identity and document verification.
• Transaction Monitoring
  Real-time analysis of transactions using behavioral analytics and customizable risk thresholds to detect anomalies.
• Device Intelligence
  Deploy a unified solution across platforms (JavaScript, iOS, Android) for consistent device ID tracking, comprehensive attribute collection, and Account Takeover (ATO) attempt monitoring.
• Customer Journey Integration
  Develop risk-based customer categorization, allowing for customizable verification steps for high-risk users while reducing friction for low-risk customers.

To thrive in today’s competitive market, fintech platforms must not only stop fraud but also integrate seamlessly with existing tools, optimize customer experiences, and enable scalability for future growth. Several core principles guide fintechs that are winning the fight against fraud.

6 Core Principles & Considerations

• Holistic Approach
  Address fraud across all areas of your business, not just in silos (e.g., ACH, credit cards, ATO).
• Proactive Prevention
  Prioritize preventing fraud before it happens, not just reacting to it.
• Customer-Centric Approach
  Use fraud prevention to create a frictionless user experience.
• Flexibility and Scalability
  Ensure cloud-native deployment options (e.g., AWS compatibility), API integrations with existing tools (ERP, CRM, communication platforms), and data portability for storage and management.
• Team Collaboration
  Foster communication and collaboration across teams (risk, security, product, marketing, etc).
• Continuous Adaptation
  Recognize that fraud tactics evolve, and your solutions must adapt with them.

Whether it’s leveraging advanced AI, orchestrating data intelligently, or aligning teams with unified workflows, the right strategies and technologies can make all the difference.

At DataVisor, we’re dedicated to helping fintechs stay ahead with comprehensive, adaptable fraud and risk solutions tailored to their needs. Learn more about how DataVisor can help.