This year, it is estimated that fraud will cost merchants 1.8% of their total revenue−up 3.5x from 0.51% in 2013.[i] Increasing rates of fraud have been compounded by the proliferation of e-commerce and card-not-present transactions,[ii] which have become subject to rising threats as the rollout of EMV chips and secure readers have made in-store fraud more difficult to commit.[iii]
In June 2017, First Data, a global leader in merchant-oriented payment processing technologies, launched Fraud Detect, a comprehensive fraud scoring solution that utilizes machine learning to measure levels of fraud risk across its customers’ payment transactions.[iv] As online threats have become increasingly complex and virulent, the use of machine learning to improve fraud detection has taken on immense importance for payment processors like First Data.
Process Improvement through Machine Learning
Unlike traditional threat detection measures, which typically rely on manual rules-based engines, machine learning allows for the dynamic detection of new, potentially unknown threats. While rule sets are excellent at detecting known threats based on past bad behaviors (see Exhibit 1), most rules-based engines are inherently manual, reactive, and static−leaving merchants exposed to risk as sophisticated fraudsters inevitably learn to evade these rules.
Exhibit 1: Traditional Rules-Based Engines in Online Fraud Detection
First Data believes that machine learning offers significant advantages in the arms race against fraud. Rather than requiring constant and manual setting of new rules or boundaries in response to new threats, machine learning can automatically and proactively detect more detailed and accurate relationships in massive data sets by searching for anomalous behaviors, as opposed to a pre-defined set of bad activities (see Exhibit 2).
Exhibit 2: Machine Learning’s New Approach: Anomaly Detection
First Data’s Strategy
Since the launch of Fraud Detect, First Data has been focused on deploying its solution across its existing customer base of over 6 million merchant locations.[v] By signing up more merchants onto the platform, First Data will not only establish a competitive moat around its fraud solutions, but it will also improve the quality of its algorithms, as precision improves with more transaction data. Processing over 93 billion transactions annually, First Data has the potential to better discern patterns and predictive measures through rigorous analysis than competitors who have more limited access to data and merchants.[vi]
In this near-term expansion plan, speed-to-market will be critical. First Data should focus first on signing up its largest merchants (possibly even at steep price discounts), who represent a disproportionate share of transaction volume, to get a critical mass of data quickly. In conjunction, First Data should target merchants across a variety of verticals and regions to understand the potential nuances in fraud risk across industries and geographies. Most importantly, the merchants that First Data does sign up for its Fraud Detect platform should all be contractually required (or financially incentivized) to report back actual instances of fraud to help close the feedback loop on First Data’s algorithms and improve their predictive power.
Longer-term, First Data is setting the foundation to grow beyond a global payment processing company and become a “market leader in security and fraud.”[vii] As we see with the launch of Fraud Detect, First Data is looking to develop homegrown competencies in machine learning to leverage its massive amount of payment data and establish broader capabilities in big data.
To achieve this vision, First Data should focus on how Fraud Detect can be complemented with other fraud detection methods over time. As the industry adage goes, there is no “silver bullet” for beating fraud−fraudsters have always found ways around past detection measures. While threats will continue to evolve, perhaps even with machine learning-driven countermeasures from fraudsters, remaining focused on exploring new frontiers for combating fraud will be key to First Data’s long-term success.
Ultimately, machine learning will continue to be a critical component in First Data’s product development road map, especially within its emerging suite of security and fraud solutions, but key questions remain:
- Given that fraud constantly evolves and finds ways to circumvent new detection measures, how will fraudsters potentially adapt to machine learning techniques?
- How much fraud can be avoided, practically? How much will fraud just be a cost of doing business in the digital era?
[i] LexisNexis Risk Solutions. 2018 The True Cost of Fraud. Accessed 13 Nov 2018.
[ii] Card-not-present transactions typically refer to online transactions, as opposed to card-present transactions, which generally take place in-store or offline, where merchants can verify the details and existence of a card and / or cardholder.
[iii] Groenfeldt, Tom. “As EMV Chips Make In-Store Fraud Harder, Fraudsters Move Online”. Forbes, 2018, https://www.forbes.com/sites/tomgroenfeldt/2017/02/28/as-emv-chips-make-in-store-fraud-harder-fraudsters-move-online/#75ffbef6493a. Accessed November 2018.
[iv] “First Data Launches New Merchant Solution to Prevent Fraud Across All Commerce Channels”. First Data, 2017, https://www.firstdata.com/en_us/about-first-data/media/press-releases/06_01_17.html. Accessed November 2018.
[v] First Data Corporation, October 29, 2018, Form 8-K, p. 6, www.sec.gov/Archives/edgar/data/883980/000088398018000043/a9302018newexhibit991.htm. Accessed November 2018
[vi] First Data Corporation, June 12, 2018, Analyst/Investor Day Transcript, via CapitalIQ. Accessed November 2018.
[vii] First Data Corporation, June 12, 2018, Analyst/Investor Day Presentation, via CapitalIQ. Accessed November 2018.