Humans invented artificial intelligence, so it is an unfortunate reality that human biases can be baked into AI. Businesses that use AI, however, do not need to replicate these historical mistakes. Today, we can deploy and scale carefully designed AI across organizations to root out bias rather than reinforce it. This shift is happening now in consumer lending, an industry with a history of using biased systems and processes to write loans. For years, creditors have used models that misrepresent the creditworthiness of women and minorities with discriminatory credit-scoring systems and other practices. Until recently, for example, consistently paying rent did not help on mortgage applications, an exclusion that especially disadvantaged people of color.

In Season 4 of the show Silicon Valley, Jian-Yang creates an app called SeeFood that uses an AI algorithm to identify any food it sees--but since the algorithm has only been trained on images of hot dogs, every food winds up being labeled "hot dog" or "not hot dog." While Jian-Yang's creation may seem absurd, in fact his app displays an intelligence that most AI models in use today do not: it only gives an answer that it knows is 100% accurate. In real life, when you ask most machine learning algorithms a question, they are programmed to give you an answer, even when they are somewhat or entirely unqualified to do so. The data on which these models are trained may have nothing to do with the specific question being asked, but the model delivers an answer anyway -- and as a result, that answer is often wrong. It's as if SeeFood tried to identify every food based only on a knowledge of hot dogs. This issue, known as "model overconfidence," is a key reason why many AI deployments fail to meet their business objectives.

Throughout history, society has debated the morality of debt. In ancient times, debt--borrowing from another on the promise of repayment--was viewed in many cultures as sinful, with lending at interest especially repugnant. The concern that borrowers would become overindebted and enslaved to lenders meant that debts were routinely forgiven. These concerns continue to influence perceptions of lending and the regulation of credit markets today. Consider the prohibition against charging interest in Islamic finance and interest rate caps on payday lenders--companies that offer high-cost, short-term loans.

The ready availability of large volumes of internal, external and social media data, along with advances in analytics and the advent of machine learning (ML), appear to have created the perfect opportunity for improving consumer lending decisions. Can it make the processes that financial institutions use to ensure they are meeting both the stringent regulatory requirements and the ever-changing consumer demands more efficient, while reducing credit risk? These questions were among the topics discussed during a recent webinar conducted by Protiviti's advanced analytics practice leaders and attended by more than 200 people. During the webinar session, we asked the attendees to weigh in on whether machine learning is currently used in their consumer lending business. Just 10.9 percent said yes, while half indicated they did not know whether their organization is using machine learning.

Internal lending processes must be transformed to eliminate friction and unneeded steps, with advanced analytics supporting proactive loan decisions. The biggest opportunity in the marketplace is the ability to continuously evaluate a customer's credit worthiness and need based on artificial intelligence (AI), including a pre-approval loan amount on the front screen of the online and mobile banking app and in alerts. The transformation must look at all current processes, refining what was done to better accommodate a digital consumer, using data, advanced analytics and artificial intelligence to eliminate steps and improve the process. The future of digital lending will reduce the friction associated with the borrowing process, eliminating paper and moving all of the steps of the customer journey to an online and mobile capability.