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Don’t Buy A Fraud Prevention Solution Before You Consider These Three Questions

Written by Ajit Ghuman, Director of Product Marketing, Feedzai, Inc.

At Feedzai, we power banking and commerce by scoring risk in real-time. We help Chief Risk Officers, Chief Financial Officers and Heads of Procurement navigate strategic buying decisions for enterprise fraud management technology. Today we want to address a recurring pattern across such complex decisions.

We often find that these cycles initially start when a remedy is sought for a symptom of the disease. A part of the business feels immediate pain, which then triggers the search for a point solution. New information on potential solutions emerge, and a decision maker changes the scope of the search so the team can find the right strategic fit.

The challenge for the buyer is real. What do you do when all vendors say the same things? What do you do when their marketing materials look the same? How do you ensure that your choice is truly a fit for your business?

To find the right strategic fit, you should ask three key questions in your evaluation:

  1. Will it scale with my business?
  2. Will it sustain its edge in five years?
  3. Does it prevent or promote vendor lock-in? 

You’ve probably heard these questions before. Let me elaborate on how they specifically relate to buying a fraud prevention solution.

1. Will it scale with my business?

We often see vendors offer solutions that do a decent job of solving a point problem but don’t scale to tangential use cases.

You may think that this is a startup company problem, but it’s much more acute for large vendors who’ve expanded via acquisitions. Their marketing collateral is often the only thing gluing multiple products into an offering. Solving new use cases with these vendors is like buying multiple new products; you make another capital expenditure, endure the months-long wait of implementation time and assume the operational risk of having internal teams learn yet another proprietary tool kit.

The benefit of having a scalable solution is to reduce long-term costs and risks. Look for platform players who are focused on solving your problem today but can also easily extend into tangential use cases tomorrow.

At Feedzai, we ensure scalability by following a platform-based product strategy. We first built a scalable data science and machine learning platform and then focused on fighting fraud as an initial beachhead. Over time, our extensible platform has allowed our clients to expand into newer use cases with innovate digital banking products, online marketplaces and more.

2. Will it sustain its edge in five years?

Legacy fraud management solutions were built for a time when banking was mostly physical; mobile wasn’t a thing, big data wasn’t captured and statisticians ran research reports. These players built their solutions for traditional banking infrastructure and plumbing such as ACH and wire transfers.

Feedzai was built at a time when tremendous changes were afoot. Banks were already closing their branches, customers expected the world on their mobile phone, big data was everywhere, machine learning started delivering results and statisticians were being replaced by data scientists. Additionally, fraudsters ran sophisticated operations with the help of large-scale computing.

To illustrate this point, see the growth interest of data scientists as a search term over time.

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Or, the surge in online banking in comparison to bank branches.

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As a result, Feedzai was built very differently:

  • It doesn’t rely on traditional relational database architectures that can’t deliver real-time performance with big data. Feedzai features a complex event processing engine utilizing NoSQL databases, enabling real-time analytics at big data scale.
  • It isn’t siloed or channel specific. Feedzai can ingest, process and build models from any data type (e.g. device id, biometrics, location) and any source (internal, third-party data vendors, social sites, etc).
  • It doesn’t separate the modeling and runtime aspects of data science, a gap that keeps models old and stale. Feedzai’s single platform for modeling and runtime delivers the ability to iterate and put hundreds of models into production within weeks.

When we got started, we used technologies way ahead of their time (Cassandra, Hadoop, Machine Learning, etc.) that are now yielding mainstream impact and we continue to invest in technologies that will power the future (e.g. Spark).

3. Does it prevent or promote vendor lock-in?

In the fraud and risk management space, legacy vendors are also monopolies. Adding new use cases is generally a multimillion dollar value proposition. While it’s good to have a one-stop shop, entrenched vendors often overprice multiple times over competition, not to mention additional expensive consulting services to patch the original gaps of their products in the first place.

We once worked with a client that was already spending upwards of $20 million with a large vendor before looking for a solution for new use cases. For this client the choice was to spend an additional $1-2 million with their existing vendor or $1-2 million with a newer vendor. In the end, they chose to partner with the newer vendor to not only acquire cutting edge technology but also to make the incumbent work harder for its money.

When looking for new solutions, its wise to hedge the risk of vendor lock-in by approaching technology additions from a portfolio lens. This ensures healthy competition between vendors, and incentivizes them to not only get, but also to keep, your business.

In summary, buying enterprise fraud management is complex endeavor and unique to every individual company. And while you can’t always predict future needs or technology trends, asking the right questions about the long term implications of such investments can greatly reduce future pain and risk.

Want to learn more about Machine Learning for Fraud Managment?

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I lead Feedzai’s Product Marketing for enterprise and financial services customers. I’m excited about the massive impact that the next generation of machine learning based platforms are having in the market. I joined Feedzai from another software firm in the Big Data and Machine Learning space where we grew from 115 to 901 employees. At Feedzai, I’m firing up the growth rocket engines again and having fun doing it.