Five things to look for in fraud detection software and how to pick the right one
The growing adoption of online services--from mobile banking, on-demand services, e-commerce, instant credit, on-demand healthcare--is providing the impetus for a corresponding increase in online fraud. The fraud management industry is a rapidly growing field; per Fortune Business Insights, the global fraud detection and prevention market is projected to grow from USD 30.65 billion in 2022 to USD 129.17 billion in 2029, exhibiting a CAGR of 22.8%.
The impact of fraud on a business can be profound; besides the obvious financial losses, it also erodes customer trust, damages the company brand, and increases operational costs. Unfortunately, businesses that don’t adapt are at an increased risk of experiencing even higher fraud losses. That’s where fraud detection software come in. The best way to prevent fraud and curb fraud losses is to partner with a fraud detection company to manage risk and establish trust across the customer journey.
But because fraud detection software can be difficult to understand and select, this article aims to demystify the process. In this article, I'll go over the top fraud detection software on the market, their strengths and weaknesses, and how to pick the right one for your business.
What is a fraud detection software
Fraud detection software is a tool that is used to find fraud and financial abuse. It usually performs a variety of fraud-related tasks, such as identifying unusual activities, analyzing data patterns, addressing adversarial patterns, and generating reports.
Overall, the best fraud detection software delivers the following key benefits:
- Provide a frictionless and safe customer experience
- Accept good users and increase revenue by reducing false positives
- Reduce the financial losses from criminal and friendly fraud
- Build customer trust and reduce brand damage as the result of fraud
- Decrease the operational costs associated with fighting fraud
5 things to look for in a fraud detection software
Integrated Machine Learning and rules approach
As new adversarial patterns emerge, decisioning workflows must evolve instantly to address them. Rules lend themselves to changes easily, making them a good fit for mitigating new fast-evolving adversarial patterns. However, as fraud patterns adapt to human-defined thresholds, the complexity of features and their corresponding thresholds used by rules can increase rapidly necessitating the use of Machine Learning models.
The best fraud detection software does not just offer black box ML models with a fraud score but employs an integrated Machine Learning and rules approach, allows you to use your custom ML models with customizable rules, and offers a platform for performing feature engineering to train your ML models. Furthermore, it also makes the same features that you trained your ML models on available to your rules.
Customizable rules engine
As much as opinionated data formats make applying a fraud detection software for a specific use case easier, it fundamentally limits the user's ability to tailor the fraud detection software to their data, business needs, and unique fraud detection patterns. Fraud detection software that allows a company to send data in a flexible format via a REST API, offering the ability to transform the data, create aggregates, and historical lists using the data, and then make that data available to the rules engine is a core need for enterprise companies.
Comprehensive testing and trial deployment functionality
What most fraud detection software often miss is the ability to backtest new changes and gradually deploy them. In the same way that you never deploy software applications without testing them, you rarely want to deploy a new change to your fraud ML model or rules without knowing how they will perform in practice. This ability to accurately backtest new decisions is surprisingly difficult to build and hence capability rarely available in conventional fraud detection software.
The best fraud detection software integrates historical data with real-time data, replays rules by running them on past data, and allows you to observe the match rate and matched data to validate the change and iterate on it. It should also offer trial mode deployment where the new fraud detection logic runs on live traffic but doesn't take effect; a necessary step to study how the change will affect the most recent user transactions. After assessing the impact of the new fraud detection logic in trial mode, the fraud detection software must also allow you to canary the changes into production by gradually turning the dial from 0% to 100% of user requests rather than impacting all users abruptly.
Data integration and fraud analytics
There is a clear divide in the fraud detection software market; some support visualization of data to find new fraud signals while others allow you to decision on fraud signals that you have already found. However, to increase the speed and accuracy of fraud detection, you need both. The data that was used to find a new fraud signal should also be used to then decision on it.
The best fraud detection software integrates real-time fraud analytics and visualization with decisioning workflows, allowing you to:
- search through user activity logs
- slice and dice data + visualize patterns
- use graph analytics to flag entities that have similar characteristics
- use the fraud signals discovered from the above processes to train ML models and run rules
Data-driven case management
Lack of the right data at the time of decisioning has a profound impact on a key metric tracked by fraud and risk teams -- the time spent on manual reviews. A majority of the time spent by analysts is on collecting data related to the case. A plethora of case management tools exist in the market, but few have the real-time data analytics capabilities that truly make fraud and risk operations efficient. You need graph analytics capability to analyze group behavior of related entities, ability to pull in data from 3rd party tools, and real-time data analytics capability to assess past user behavior, in conjunction with case management capabilities.
How to Choose the Right Fraud Detection Software for Your Business
Thus far, we discussed what is fraud detection software and the key capabilities of the best fraud detection software, but how do you choose the right one for your business?
Here are a few factors to consider:
Complexity of independent fraud detection tools
The conventional approach to addressing fraud has been to layer a slew of independent fraud-prevention tools and systems on top of one other, each with its own set of capabilities. The result is a fragmented fraud stack, often characterized by redundancies and cost inefficiencies.
The core problem with this approach, other than the increased complexity, is the inability to use an evolving 360-degree view of the user data for mitigating risk throughout the customer journey. After all, if a user has a high account risk, that should factor in the risk assessment for the user's payment transaction.
Platform approaches that have come into the market in recent years have sought to address this. Selecting a comprehensive fraud detection platform with a flexible API and customizable decisioning engine is paramount to your ability to accurately model user behavior and apply it for assessing risk throughout the customer journey.
The need to consult multiple 3rd party tools
Often, fraud detection tools offer a single fraud score based on proprietary data and algorithms. But do you want to depend on one company's data or would you rather consult multiple data sources? Increasingly, companies are opting to consult multiple 3rd party tools for various fraud scores in conjunction with internal data for holistic fraud detection capability.
The best fraud detection software employs a platform approach -- the ability to leverage multiple data sources and fraud scores versus a single one based on their own proprietary data.
Time spent on accessing the right data
We surveyed dozens of fraud and risk professionals and the feedback was resounding -- a majority of the time in fraud detection operations is spent on assembling the right data that is typically fragmented across various databases, applications, and the data warehouse. Often fraud detection tools lack crucial data integration and analytics functionality that is instrumental in cutting down the meantime to mitigation for addressing adversarial fraud patterns.
A comprehensive fraud detection solution must have no-code data integration functionality that allows non engineers to easily pull in all the right data from various internal data systems and 3rd party tools alike. And have the ability to integrate external and internal data and make it available to ML model training and running custom rules.
The need for fraud analytics
Finding a new adversarial pattern is like finding a needle in a haystack; you need to sift through a lot of data in real-time, slice and dice it, and visualize the patterns to spot a new fraud trend. Moreover, spotting a new fraud trend is followed by decisioning on the same set of signals. At present, fraud analytics is done on the data warehouse using SQL in a batch fashion while scoring takes place on a decisioning engine. Data from the SQL data warehouse is different from the data used by the decisioning engine fundamentally impacting the accuracy and speed of the fraud mitigation process. The fraud detection tools market is fragmented between tools that do analytics or visualization and tools that do fraud scoring and decisioning. However, accurate and timely fraud detection requires both to work in tandem and in real-time.
Time spent on manual review
A core issue for fraud and risk teams is efficiency concerns driven by the growing number of cases sent for manual review. Basic operational improvements and investment in enhancing review workflow tools may help reduce the time and money invested internally on manual review, but they merely address the symptoms rather than the problem itself, and therefore don't address the foundational issues -- a) the lack of comprehensive data available at the time of decisioning causing more cases to fall in the gray area, that then get routed for manual review b) the lack of real-time data available to the case reviewer to accurately and swiftly evaluate the case.
Fight fraud with a well-rounded fraud detection software
To continue modernizing your business by moving critical customer interactions online while also dealing with the complexity of adversarial patterns, there is an urgent need to pick a well-rounded fraud detection software. One that is not only comprehensive in the functionality it provides but also sufficiently customizable to adapt to your company's evolving business needs. After all, balancing fraud losses with friction in the user experience isn't a one-time decision.
In this article, we've looked at five things to look for in a fraud detection solution, as well as, a guide on how to pick the best one for your business needs. If you would like to learn more, we'd love to chat.