In many organizations, big data and analytics reside somewhere between the IT organization and marketing. That’s primarily because of the technical barriers around the stop-and-start nature of most analytics programs. To get an answer to a business question, someone — typically with some technical chops — has to write a query on the data to get at the answers.
That’s changing. For organizations to leverage the power of business intelligence and analytics, tools and systems are becoming easier to use and more real-time. Rita Sallam, research vp at Gartner, explained, “The BI&A market is in the final stages of a multiyear shift from IT-led, system-of-record reporting to pervasive, business-led, self-service analytics.” Once BI tools don’t require programming skills or a degree in statistics, their mainstreaming is practically guaranteed because they can add dollars to the bottom line.
One startup that’s taking this new approach is Anodot. I first heard of the firm in September (when it announced its most recent funding round), and I assumed the company was in cybersecurity because it does something it calls anomaly detection. But after speaking to David Drai, cofounder and CEO of the Israeli startup, it became much clearer that the company’s technology addresses big pain points in fintech.
Anodot’s machine learning looks at different buckets of data in an organization to establish a view of what normal looks like. So, if you’re processing credit cards, for example, Anodot examines approval rates across geographies to develop a model for what defines business as usual. Any deviation from the norm triggers alerts to relevant departments. The technology correlates abnormalities in the data to external events, like a new product launch, to see what’s causing them.
There’s no need to write hundreds of queries. “Our machine learning does this all for you,” said Drai, who previously cofounded Cotendo, which was bought by Akamai in 2011 for $268 million. “Anodot can answer two important questions for you: what’s happening and why.”
Credit Karma came to Anodot in 2016 with a specific problem. The company relies upon numerous funnels to ensure prospects and existing clients most efficiently interact with the firm’s credit scoring and monitoring service. All of a sudden, the revenue driven by a certain webpage dropped by over 50 percent over a three day period before the company could determine what was causing the problem.
“To test Anodot, we streamed a subset of six months of historical data to see if Anodot would find the same anomalies we had found manually, and it did,” said Pedro Silva, a senior product manager at Credit Karma. “It was clear very quickly that Anodot provides a ton of value to both business and technical teams.” After working with Anodot, Credit Karma quit further development of its own in-house solution to eliminate business incident detection latency.
Riskified is another fintech firm that uses Anodot’s real-time business dashboards. The company works with large retailers to prevent ecommerce fraud with instant approvals and full chargeback protection. Because it assumes the approval risk from its merchants, a break down in its risk models can be disastrous. The company looked to outside vendors for assistance.
“We think that someone focused on something specific will almost always do it better than generalists,” said Assaf Feldman, Riskified’s CTO. “Our customers know ecommerce really well but they turn to us for state of the art fraud protection. We’re all willing to pay for specialization.”
Tel Aviv-based Riskified uses Anodot to monitor internal signals of its fraud models and to make sure service levels are maintained for the product. The system’s alerts notify account managers if there are problems.
As Riskified looks to broaden its usage of Anodot across the company, fintech firms are taking a hard look at using new business intelligence and analytics tools in 2017.
