The stories are familiar now. Stolen credit cards for sale on the dark web. State-sponsored attacks. An almost overnight shift in attacks to Payroll Protection Program (PPP) scams, courtesy of the pandemic.
But despite the billions spent by organizations to combat financial crimes, fraudsters are still gaining ground. Why?
Traditional FinTech ‘crime fighting’ solutions are broken
Some FinTech solutions are too complex. Suppliers tout algorithms developed with DARPA, NSA data scientists or elite universities. And POCs show promise.
Implementation is a different story.
More than 80% of machine learning projects fail. Custom application pipelines create long, complex pathways to production. IT efforts get tied up maintaining data feeds and application “pipeline jungles.” Innovation stops and the fraudsters gain ground.
Other solutions aren’t advanced enough, and organizations are faced with two options:
- Tune too tightly and attempt to manage a flood of false positive alerts
- Tune too loosely and allow fraud to slip through
Crucial decisions end up being made based on the number of alerts that can be processed in a day—not the actual number of alerts received
So, what do you need to catch a clever criminal?
#1: Get ALL the data
“More data” isn’t enough anymore.
Organizations must collect—and organize—data that fraudsters don’t know about or expect to be tracked. Data in context helps both software and users better understand and identify the differences between illicit and benign behaviors.
That might include:
- Spending and payments across all products and channels
- Non-financial interactions through branches, call centers, and mobile and digital channels
- Biometrics (finger size, pressure, click speed, angle of device, etc.)
Some vendors argue integrated data isn’t necessary—or that “dirty” data is okay. But failure to integrate your data on the back end pushes the task to individual teams. The result: inconsistent outcomes, higher project costs, and longer audit and compliance cycles.
#2: Build flexibility into processing
Fraud is complex. It doesn’t rely on a single model or technique. That’s why it takes hundreds, thousands, or tens of thousands of models—with appropriate rules, statistical methods, and supervised and unsupervised machine learning—to build effective fraud defenses.
Fraudsters also change their attack patterns as new opportunities arise.
To stay agile, organizations need a shared data platform that enables their team members to answer any question—without needing to copy or move that data to a different tool.
For example, a bank could use the same data to determine a person’s credit risk or whether they’re overextended with current borrowing. Changes in customer spending could also indicate account takeovers or credit bust-out fraud.
#3: Keep one step ahead with advanced analytics
The best way to outsmart tech-savvy criminals is with solutions that can:
- Predict when the crime will happen
- Pinpoint where it will happen
- Prevent the crime from happening
To do that, analytic solutions need the power to run the most demanding analytic workloads—entity resolution, network analytics, and predictive models.
Agile platforms make it possible to leverage all your data. They also let you take full advantage of advanced capabilities like AI and machine learning.
Moving towards a crime-proof reality
Financial crimes are here to stay. But fighting fraudsters isn’t just a matter of investing more money in analytics.
Organizations need to invest in the right solutions to outsmart tech-savvy criminals. Financial services companies require a connected multi-cloud data platform to cut through complexity and deliver useful, actionable answers to any problem.
Want to learn more about “crime-proofing” your organization? Sign up for our Financial Crimes webinar series.