The Strategic Value of Qa Data in Financial Services

The Strategic Value of QA Data in Financial Services

By Derek Corcoran, CEO of Scorebuddy, the world’s leading quality management platform for contact centers

Quality Assurance (QA) has multiple roles within the financial sector. It doesn’t just help with compliance and the evaluation of customer service and products, it has the potential to shape every element of a business, from the customer journey lifecycle to cost management and employee engagement This makes QA data key to practical and constructive decision-making across the board, but how can you maximise its strategic value?

Understanding and using QA data in finance

In finance, QA is typically the process of measuring, evaluating, and reviewing the services, products, and systems provided by and used throughout an organisation. Sometimes referred to as Quality Monitoring or Quality Audit, it is a systematic approach to ensure that standards are always met, and that prompt action can be taken when they are not, but that is not the limit to its potential remit.

The strategic potential of QA in financial services

Most QA programs focus on specific customer service and agent performance metrics, but if this data is properly applied, it can be used to strategically enhance a complete range of business procedures and KPIs.

• Customer experience, satisfaction, and retention – With the right QA insights, you can quickly and easily identify any negative issues within the customer journey, while enhancing personalisation and customer experience (CX), enabling the attainment of a higher CSAT. Because greater satisfaction typically leads to greater loyalty, there is a de facto increase in customer retention.
• Risk mitigation and credibility – Poor risk management has the potential to permanently damage the reputation of a financial business. We only have to look at the fallout from and casualties of the Global Financial Crisis (2007-2009) to understand the truth behind that statement, but effective QA processes minimise risk and ultimately enhance organisational credibility.
• Operational efficiency – QA can provide an immutable basis for the establishment and maintenance of company guidelines. Providing data-driven insights, QA allows you to define expectations and create systems for achieving them, streamlining operational efficiency with minimal effort.
• Team training, productivity, and attrition – QA scorecards enable managers to easily identify areas where additional training may be required, allowing for the creation of relevant training programmes or individual coaching and this has a direct impact on overall business productivity. Better training opportunities also lead to enhanced job satisfaction, which can reduce employee attrition.
• Regulatory compliance – The financial services industry has some of the toughest compliance standards of any industry. Failure to adhere to them can not only result in hefty fines, but other penalties and reputational damage. QA system tools, such as call recording and scorecards, allow business to monitor compliance and address issues quickly.
• Cost reduction – In 2022, the number of Financial Conduct Authority (FCA) fines more than doubled, reaching a value of £215,834,156. Through compliance alone, QA can help businesses reduce their overheads. When you add in efficiency, productivity, attrition, and all the other operational enhancements, a strong QA strategy can save organisations money.

Why don’t more financial organisations use QA data in this way?

While it’s easy enough to say that QA data holds all of this potential, there is one glaringly obvious problem. QA data comes from a huge range of sources and the difficulty lies in accessing all that pertinent data for any one particular problem and bringing everything you need together in one place for simple report writing, decision-making, and strategic planning – that’s where having the right technology can help.

Technology and QA data

The evolution of technology in recent years has been mind-blowing. From machine learning (ML) to artificial intelligence (AI), the advancement of tech seems unstoppable and this has meant significant improvements in QA management. With the right tools, it becomes possible to access and manage multiple metrics and datasets simply and efficiently. By allowing users to choose how they view metrics, move them around, drag and drop multiple sources into a single report editor, even the most complex, multi-source investigations become simplified This enables not only cohesive report creation, but improved decision-making, and enhanced company-wide efficiency.

Of course, technology can’t work alone. At least, not to its best advantage. The savvy business will also focus on employee training and engagement, using and supporting their people to achieve the best results and most satisfaction for all. They will also keep an eye on the wider industry, seeking to anticipate and prepare for change, staying ahead of the game rather than reacting to it.

QA data can be a vital and almost limitless resource for financial organisations. The challenge is effectively handling it. Leaving many businesses not only struggling to reap the full rewards of this data goldmine but wasting time and energy on unnecessarily complex reporting processes. Technology holds the power to rectify that situation.