As the globe focuses on consumer justice and financial inclusion, financial institutions may provide additional monetary relief to clients unless they focus on mitigating bias-related customer problems. Within the CFPB database, complaints addressed through monetary relief reveal a bias of 50% more than all other firm's replies to consumers. Additionally, the bulk of complaints addressed with monetary compensation reflects extreme customer dissatisfaction. By December 2020, the CFPB's public enforcement efforts would have resulted in more than $12.9 billion in total consumer relief - a figure that is projected to grow.

Combating bias begins with data-driven awareness and recognition and scores and algorithms that enable institutions to measure – and mitigate – bias accurately. In addition, banks must view customer complaints as a credible source of information. Unstructured data combined with skilled analytical rigor and contextual information about the business provides structure and solutions to complex problems. In addition, customer complaints provide predictive data that can assist financial organizations in avoiding high-risk situations.

To combat institutional bias, businesses can analyze customer complaints using advanced artificial intelligence to identify patterns, predict outcomes, and reach conclusions that benefit both the customer and the institution.

Banks may use artificial intelligence to detect the small – but crucial – percentage of customer complaints related to discrimination and offer proactive management actions to alleviate these pain points. These measures improve customer service, compliance with regulatory and legal requirements, and overall business performance benefits.

Bias can manifest itself subtly and through a variety of channels. Customers who encounter an impediment may discover that it is caused by discrimination. Discrimination against customers may occur based on their color, gender, religion, sexual orientation, age, citizenship, or military service.

Bias may be overt, covert, or implied. Customers express their experiences of discrimination, although frequently indirectly, through their complaints. By converting customer complaints to data and utilizing modern analytics capabilities, banks can extract the insight necessary to uncover, mitigate, and quantify prejudice - ultimately resulting in more consumer fairness.

Diversity and inclusion-minded leadership are critical. For example, there is a correlation between satisfied customers and customer-facing staff, and employee discontent can be understood using the same tools as customer frustration. In addition, leaders may empower all staff to speak up and bring attention to pressing issues.

Today, the instruments exist to ascertain the source of customer dissatisfaction, which may include bias and discrimination. In addition, banks may use artificial intelligence to evaluate unused data, gain insight into financial institutions' most costly pain areas, and determine where they should intervene. By integrating business domain expertise and modern analytics, institutions may make proactive policy and procedure changes that benefit both the client experience and their bottom line.

About Jim Woods

Jim has more than two decades of experience driving change around diversity, equity, inclusion, performance, growth, and innovation. He's designed and led complex transformation initiatives in companies linked to globalization, demographic changes, sustainability, shifting business models, and new technologies.

Earlier in his career, Jim served in the United States Navy, taught fifth-grade math and science, including university human resources and leadership. Also, Jim has taught at Villanova University. He has authored six business books on DE&I, and leadership.

Education

Capella University, MS in Organizational Development and Human Resources