How leveraging a digital adoption solution can help reinforce the assurance of quality

Emerging technologies are transforming the landscape of today’s BFSI industry at the speed of light. Applications leveraging artificial intelligence (AI), machine learning (ML), blockchain, and robotic process automation are changing the face of banking operations and customer experiences. From customer-facing virtual assistants to advance analytics providing real-time actionable insights to robust cybersecurity solutions with biometrics and behavioral analysis software, the adoption of advanced technologies is gaining tremendous momentum in banking sector operations. However, this transformative advancement in banking comes with a cost. The increased reliance on technology to provide efficiency and performance requires a more stringent focus on the cost of quality, with poor quality resulting in significant risks and heavy penalties.

What is cost of quality?

Cost of Quality (CoQ) allows companies to examine the balance between how much of their resources are spent on prevention and maintaining standards versus how much internal and external quality failures cost the company. Companies often struggle with their existing quality control mechanisms that incur high costs while providing low coverage of comprehensive quality management. Take, for example, the Australia-based Bank of Queensland. The organization had to refund $34.5 million to its customers after discovering interest rate and fee errors dating back almost a decade. This instance clearly shows the criticality of quality assurance in business growth and customer satisfaction, causing a significant impact on the top and bottom line if not done right. Human errors cause financial losses and result in poor customer experience, reputational damage, lower share price or money, and time spent responding to regulators.

Various issues can directly or indirectly cause human errors:

• Lack of qualified and experienced staff
• Oversights in mundane, repetitive tasks
• Lack of awareness of changes in underlying applications
• Outdated SOPs
• Insufficient information for proper monitoring or audit, including all possible datasets
• Heavy reliance on manual effort 

How can technology reduce errors?

How do we minimize errors and achieve operational efficiency? Accelerating quality management capabilities involves replacing expensive and poor quality control mechanisms with solutions that increase quality assurance and reduce the spending on quality. To achieve this, we can either automate manual processes to accomplish accuracy or prevent humans from making mistakes. Process automation by leveraging robotics and workflow engines can replace manual processes that are standardized and repetitive.

However, changing the propensity to commit human mistakes requires predictive analysis to identify the factors contributing to a higher likelihood of error. For example, at Wipro, we recently deployed a predictive analytics model to identify ~75% of the errors by merely selecting 30% of samples in one of our client’s mortgage underwriting processes. We collected twenty-seven parameters for 6787 loans processed and performed data cleansing with 2671 valid data points. The model clustered out the most likely factors to cause high critical errors. We then prompted the inputs to loan processors to work accordingly, thereby successfully reducing the critical errors from 5.6% to 1.4%.

Unlock efficiencies with a digital adoption solution

A “no-code” digital adoption solutionwhich can act as a realtime virtual SME can help users learn workflows and alert the agent in real-time to reduce the risk of human errors through various features like smart tips, auto-fill, flow automation etc. Let us explore some of the challenge scenarios to explore how this solution can resolve the issues:

Conclusion

Human errors can prove to be really expensive. Did you know that in 2020, financial institutions took a hit of $10.4 billion in global fines & penalties related to AML, KYC, Data Privacy & MiFID (Markets in Financial Instruments Directive)? Most organizations have blamed technology & human errors for these heavy penalties. Also, one of the largest banks in the US paid $900 million to lenders due to “human error.” This is not all. From 2014-to 2019, 83 banks have reported 778,639 loss events, totaling a staggering amount of $533 billion.

Of all the factors that can adversely affect the cost of quality, operational errors pose the most significant risks. According to a report from IBM, bad data alone costs US businesses more than $3.1 trillion a year. These issues can be human-driven, technology-driven, or beyond human or technological control. Whatever the type may be, we can reduce the impact of these errors on the cost of quality by working on the factors within our control. Replacing your existing quality control processes with a digital adoption solution will offer both the analytics and a platform to help eliminate manual errors while optimizing costs. Improving quality, proficiency, productivity, and accuracy with a digital mechanism will also ensure process compliance, enabling businesses to improve efficiency, reduce operational costs and focus on growth.