Last week, the Consumer Financial Protection Bureau (CFPB) turned 3 years old. When the CFPB launched in July 2011, it wasn’t clear what power this regulatory progeny of the Dodd Frank Act would wield. Today, there is no doubt about the agency’s authority. With 9 products in its ever-increasing purview, the CFPB now hosts over 275,000 complaints in its public database (400,000 received in total), and has enforced 22 regulatory actions costing top financial institutions $4.6 billion dollars to date. As a result, financial services providers are hearing a serious wake-up call to monitor and improve their practices.
As part of its birthday celebration, the CFPB published a list of figures summarizing its achievements to date. For banks and other financial services companies, one of the figures left out may be the most eyebrow-raising – the meteoric rise in the dollar amount of regulatory fines.
The agency’s first target was Capital One, ordering payment of $165 million in fines and restitutions for issues such as deceptive marketing practices. Over time, the average amount of a CFPB enforcement action has increased from $160 million in 2012 to almost $280 million in 2014 – a 75% increase in only two years! Even in 2013, when the CFPB levied its biggest penalty to date – a whopping $2.6 billion to Ocwen, a leading mortgage servicer – the average fine was less than it’s growing to be in 2014. The message is clear – it’s getting more and more expensive to ignore customer feedback, and more imperative to find and fix customer experience pain points.
How not to get invited to the party? Leverage customer data
The good news for banks, credit unions, and other financial services providers is there is a clear way to avoid paying a similar costly penalty. The answer is in the data. Complaints do not exist in a vacuum. They exhibit a lifecycle, and if you trace the journey of a complaint from inception as a customer concern, there are typically multiple touch points before the customer resorted to involving the CFPB. These touch points represent multiple opportunities for a company to fix the customer experience issue and prevent an escalated complaint that could spark regulatory action.
The companies doing the best job at predictive prevention of complaints are using decision tree algorithms to find patterns in their customer data. Want to identify targets of highest propensity for escalation to the CFPB? Your business can explore which products tend to generate problems, which types of issues seem most sensitive, which types of customers might be more negatively impacted, etc. Also look at other angles such as how customer tenure affects their likelihood to complain, and how channel use plays a part in customer pain points.
The answers will be different across products and companies because the experiences differ. The solution, however, is the same:
- Journey map complaints starting with their inception as customer issues.
- Identify patterns in transactional and behavioral data that predict complaints.
- Deploy a decision tree model on current customers to assign a propensity to complain.
- Flag customers with the highest propensity to complain, and intervene before their issues escalate to the CFPB.
In the era of data science, we’re no longer analyzing data to answer the question, “What happened?” Today, we are asking “What will happen, and how can we impact it?” If banks and credit unions want to avoid paying hundreds of millions to billions in fines, it is time to embrace predictive prevention.