Improving banking services through call analysis

The steady development of the financial industry, marked by increasing wait times, the emergence of new competitors and products, and a widespread crisis of trust, has eroded customer loyalty. Human empathy remains an essential component for building lasting banking relationships, and voice analytics solutions are at the forefront of this effort. Today, improving the customer experience has become a strategic priority in all organizations, especially in the financial sector.

Customer expectations are constantly changing, and to meet these demands, the customer experience must be the focal point of banking process design. These include loan management, payment processing, and customer dispute resolution, among others.

In a decade, these administrative operations are expected to undergo a drastic transformation. According to McKinsey estimates, between 75% and 80% of transactional operations and up to 40% of more strategic activities could be automated. This implies a significant shift in the set of tasks and skills required by operations staff.

Despite the digital dominance, customers still value the human touch, particularly when it comes to more complex financial products. But, instead of focusing on transaction management or data gathering, the trend is to use technology to advise customers on financial options and products, creative problem solving, and developing new products and services to improve the customer experience.

This article explores how call analytics can be key to advancing in this direction, enabling banks to improve their services and better meet the changing demands of their customers. Thus, in the not-too-distant future, banks may more closely resemble technology companies in terms of operations and customer experience.

Main challenges of the banking sector

Financial institutions must adopt a panoramic and strategic vision to prepare for the transformative impact of digital technology on banking operations. Operations, which currently consume between 15% and 20% of a bank’s annual budget, have great transformational potential that can lead to significant improvements in profitability, return on capital to shareholders, and offering superior quality products and services to customers.

It is essential that banks rethink traditional processes, often more focused on internal needs than on those of the customer, and place the customer experience at the center of process design. By doing so, operations cease to be a cost center and become a driver of innovation and improvement of the customer experience.

Here are some of the main challenges of the banking sector today:

Creation of personalized products and services

Currently, banks offer coded and standardized products, backed by specific operational roles. However, in the future, these activities will be automated and the roles of employees will shift towards product development. Operators will work with automated systems to create flexible and personalized products, such as credit cards where users can define their own rewards or loans with personalized payment plans.

Although currently this level of customization would be a considerable operational challenge, digital transformation will allow banks to develop and manage these personalized products efficiently, transforming banking operations into a driver of innovation and customer experience improvement.

Generation of fluid processes and consistent quality

Currently, many banking operations employees perform repetitive tasks daily, leading to inevitable errors. However, automating these processes can eliminate human bias and reduce errors. This not only improves efficiency but also frees employees to address more complex and delicate problems that cannot be solved by automation. Thus, these employees will be equipped with the authority and skills to make decisions and quickly resolve customer issues.

Proactive management based on analytics

The use of predictive analytics can revolutionize banking operations management by allowing more accurate predictions based on detailed profiles of customers and their behavior. These analytics allow banks to understand the needs and wants of their customers at an individualized level, which can inform the creation of more detailed KPIs and better adaptation of products and services.

Predictive analytics can transform problem resolution, allowing banks to identify and correct errors at the level of a single customer and even take preventive actions to increase customer satisfaction. Instead of simply being reactive, banks can become proactive, anticipating customers’ questions or problems before they arise.

Simplification of organizations

Administrative offices will shrink in size, call centers could disappear with the help of artificial intelligence and automation, and bank branches will transform both in number and function. As more transactions are digitized, branch employees will act as personal advisors, responding to complex inquiries and providing advice on banking products. This new paradigm will lead to a more personalized banking experience and a more simplified organization.

Adapting employee skills to the new paradigm

Banking operations employees of the future will be different from current ones, with a focus on customer needs and a strong background in technology, data, and user experience. This staff will include digital designers, customer service experts, engineers, and data scientists and will focus on innovating and developing technological solutions to improve the customer experience. Furthermore, they will have a deep understanding of banking systems and the communication and empathy skills needed to handle exceptions and offer high-quality service to customers with complex issues.

Designing an omnichannel customer experience

To increase sales and maximize revenues, banks must implement a flawless omnichannel offering that balances human and digital interactions. This involves using data analytics to improve marketing personalization, equipping the sales force with the tools to operate in omnichannel environments, and utilizing advanced analytics for better targeting.

Through data mining and behavior analysis, banks can identify and focus on high-potential customers, thereby optimizing lead generation and achieving a balance between sales, generated value, and service cost.

Use of automation and new technologies that empower the customer

The ever-deeper penetration of automation and artificial intelligence into banking operations promises to transform not only banks’ cost structures but also the customer experience. By digitizing processes like loan fulfillment and closure, the waiting time for approvals is drastically reduced from days to minutes.

In call centers, customers can benefit from an automated and efficient customer service, powered by advanced artificial intelligence. Likewise, dispute resolution can also be improved thanks to AI and advanced analysis, allowing for quick real-time decisions based on an instantaneous evaluation of customer data and historical patterns.

5 ways AI helps banks improve customer service

Banks must make significant organizational changes to adapt to a future where traditionally isolated roles integrate into product development, product management, and customer experience. Artificial Intelligence stands out as one of the fastest-growing applications in the banking industry, as it helps to identify and ensure high quality of service to retain profitable customers. By analyzing calls and interactions across different channels, AI detects signals of potential negative customer experiences and alerts the company to take preventive action.

AI-based banking solutions leverage Natural Language Processing (NLP) capabilities to help agents better understand customers and anticipate their needs, providing assistance during calls through alerts and notifications. This drives a superior customer experience and provides a more personalized service. Moreover, AI provides valuable insights that help reduce call volume, achieve first contact resolution (FCR), and decrease average handling time (AHT).

AI-based call analytics solutions, like Upbe, are designed to enhance the value and performance of customer interactions across the banking sector, providing financial institutions with deeper business insights from customer interactions, thanks to the following features:

1. Analysis of 100% of calls

AI facilitates the analysis of customers during calls and the prediction of their behavior. By using relevant keywords, call centers can quickly detect and address problematic calls, improving customer experience, increasing loyalty, and reducing customer churn.

2. Identification of customer sentiment

Artificial Intelligence is a powerful tool for sentiment analysis in customer interactions. It allows for the identification of emotions and level of satisfaction expressed during a call, providing the opportunity to understand the customer’s mindset and study their perception of the product or service. Sentiment analysis is especially useful in deep product and service analysis, reputation management, and customer service evaluation, all crucial to improve service quality and customer experience.

3. Quality control

Conversational analysis technologies allow banking supervisors to evaluate whether agents adhere to the established script and quality standards. This quality monitoring enhances customer service and optimizes user experience. Also, not only script adherence but also reducing long periods of silence, which can hide problems that generate additional costs and gaps in service quality and customer experience.

4. Agent training

Artificial Intelligence provides supervisors with the necessary information to train their agents, identifying best practices and sales techniques. Calls analyzed by AI become formative examples for both new and experienced agents, allowing them to develop scripts based on real experiences and improve the success of their future interactions. It is especially useful for underperforming agents or those who deviate from the script, as it helps to identify and effectively manage operational and performance issues across the banking organization, improving overall customer service quality.

5. Compliance and security

Regulatory compliance is crucial in the banking industry and Artificial Intelligence is key to driving process compliance and improving overall agent performance. By scoring and automatically analyzing all calls, it reduces the risk of sanctions and litigation related to non-compliance. In addition, it optimizes identification processes, improving customer experience while ensuring regulatory compliance and security through consent record verification and identity theft fraud detection.

In short, Artificial Intelligence offers banks the opportunity to better understand their customers’ needs and preferences, providing efficient and personalized service in a highly competitive market. The following example from one of our customers backs this up:

Success story: Automatic prediction of risk and profitability for +30,000 bank loan customers

The goal of this service was to accurately and efficiently predict default risk and estimate the Return on Investment (ROI) for each customer throughout their lifecycle, in order to make informed decisions about granting loans.

A machine learning model was developed based on multiple factors that analyzed the default risk of over 30,000 loans in near-real-time. Variables such as credit history, income, job stability, and other relevant factors were taken into account. Additionally, the customer’s lifecycle was considered to assess their potential long-term profitability.

The model was fed with updated data and trained using advanced machine learning algorithms. This allowed for a real-time analysis of each customer’s default risk and made instant predictions on the amount of loan that could safely be granted.

Thanks to this approach, there was a significant reduction in the default rate. By automating the risk assessment process, decision-making in granting loans was optimized, avoiding the assignment of excessive amounts to high-risk customers.

Furthermore, by considering the long-term profitability of each customer, resources were appropriately allocated, and retention strategies were focused on those customers with the greatest potential to generate a positive return for the bank.

Overall, this AI and data analysis-based approach has improved the financial health of our client, by ensuring greater accuracy in risk assessment and more efficient management of granted loans.

Do you want to try Upbe in your organization? If you want to learn more about how our AI can drive your bank’s success, don’t hesitate to contact us!