Summary
The First American Corp. (FAF) and Verisk Analytics are competing aggressively for leadership in the mortgage fraud analytics space. This space is growing rapidly with potentially high margins. The need to invest in product improvement may depress margins near term.
Analysis
First American Corp. introduced this week a new product in the mortgage fraud analytics market with a solution coupling pattern recognition technology with public records data. This move expands First American's presence in the mortgage space and levers the company's relationships with the nation's largest lenders through its CoreLogic subsidiary.
The CoreLogic solution represents a well funded competitor to Verisk Analytics' Interthinx subsidiary. Interthinx has been the market leader and claims to score two out of ten mortgages for fraud. CoreLogic contends in a press release that it is the leading fraud prevention provider and that its solution detected significantly more fraud at operationally feasible review rates than any other alternative mortgage fraud solution provider. (What the term “operationally feasible review rates” means is unclear to this writer.)
The demand for mortgage fraud solutions has been growing as lenders become more focused on loan quality rather than origination volumes. Given the value of not originating a fraudulent mortgage loan versus the cost of scoring that loan for fraud, it is conceivable that every new loan will be scored someday.
Verisk reports its Interthinx subsidiary's results in the Fraud Identification and Detection Solutions category of its Decision Analytics segment. The fraud category grew at a rate of 26.8% for the six months ended 6/30/2009 versus prior period. Fraud detection tools are in high demand in all the businesses Verisk operates in (P&C insurance, mortgages and healthcare). This kind of growth, plus the scalability of predictive modeling businesses like these, will invite new investment in competitive solutions.
CoreLogic and Interthinx, vying for leadership in this space, announced new national mortgage fraud databases this week. Because they both access the same public records data, the key competitive differentiator is the quality of their algorithms. These algorithms are the predictive modeling tools that enable customers to glean insight and make decisions about fraud risk. Algorithms need to be continually updated to maintain a competitive advantage. Investment in model improvements to stay ahead of competition could be a drag on margin expansion in these otherwise scalable businesses.
The battle for dominance of the mortgage fraud analytics space will play out over the next few years, with no clear winner apparent now.
This author consults with leading institutions through GLG
Analyses are solely the work of the authors and have not been edited or endorsed by GLG.
