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Generative AI: Can it be Banking Backoffice’s ‘New Best Friend’?

Generative Artificial Intelligence (GenAI) is not just a buzz word anymore. It is evolving as a transformative force that is reshaping the world. 2023 has been a breakout year for GenAI startups, with equity funding topping $21.8B, which is 4X compared to 2022.

Banking and Financial Services industry has always been at the forefront of adapting disruptive technologies and developing use cases. GenAI holds immense significance in the realm of banking, ushering in a new era of efficiency, accuracy, and innovation. According to McKinsey, the technology could deliver value equal to an additional $200 billion to $340 billion annually across the banking industry.

While the limelight often shines on customer-facing applications like conversational AI, chatbots, voice bots etc., overlooking the banking back office will be a missed opportunity. In an industry where data is a cornerstone, GenAI's ability to process vast volumes of information, understand context, recognize intricate patterns, and generate meaningful insights can truly shape the future of back office.

While there is a plethora of possibilities with Gen AI, in this blog, we will dive deep into 4 select use cases from the banking back-office, that have the potential to bring a meaningful impact: 

1-      Credit risk assessment and underwriting:

Banks gather a large amount of data on applicants, including credit scores, financial history, income and expense details, and more. Traditional underwriting processes involve manual data entry and analysis, leading to delays and potential errors. GenAI steps in by automatically collecting, cleansing, and preparing data from various sources, ensuring accuracy and consistency.

Once high-quality data is available, GenAI employs advanced algorithms to extract relevant features from the data. It identifies patterns, correlations, and trends that might not be apparent through manual analysis.  This helps uncover intricate relationships that could influence credit risk. GenAI uses deep learning and creates highly complex risk models that go beyond traditional credit scoring systems, incorporating dynamic and real-time data.

GenAI generates synthetic scenarios that simulate different economic conditions, market trends, and borrower behaviors. These scenarios help assess the potential impact of external factors on loan repayment and can help banks improve the accuracy of risk predictions. It can also flag early warning signs of financial distress or defaults by analyzing transaction patterns, spending habits, and other data points.

This not only reduces the time to credit decisions but also ensures that decisions are based on data-driven insights rather than subjective and biased judgments. 

2-      Process customer instructions and requests:

Even after revolutionary changes and upgrades in self-serve capabilities, Banks are overwhelmed with volumes of customer instructions and requests that need to be processed manually in middle and back offices.

GenAI is equipped with advanced natural language processing capabilities that enable it to understand and interpret customer requests, regardless of the phrasing or language used. This includes recognizing keywords, intents, and sentiment analysis to grasp the customer's needs accurately. Once the customer's request is interpreted, GenAI can retrieve relevant data from the bank's system of records. This includes account information, transaction history, and any other pertinent details related to the customer's request. Based on the customer's instruction and available data, GenAI makes automated decisions within predefined parameters.

For instance, if a customer requests to change the billing cycle of a credit card, GenAI can fetch customer details, refer Bank’s policy, analyze similar requests, take a decision, and process the request in Credit Card system without manual intervention. In cases where customer requests involve any exceptions, GenAI can analyze the situation and generate appropriate and personalized responses. It can also propose potential solutions based on historical patterns or context. 

3-      Fraud detection, prevention, and investigation:

Fraudsters constantly keep evolving and changing tactics. Hence, fraud management systems need to be based on adaptive strategies. GenAI is a strong tool to tackle fraud effectively. GenAI doesn't rely on static rules. It uses adaptive and contextual strategies. If fraudsters change tactics, it evolves to detect new and emerging patterns and anomalies.

GenAI gathers data from diverse sources including transactions, channels, user behavior, device patterns, IP addresses, geo location data, third-party databases, historical fraud cases, external risk indicators and integrates this diverse data, creating a comprehensive and holistic view of the banking ecosystem. It recognizes patterns, anomalies, and correlations that indicate potentially fraudulent activities.

GenAI generates synthetic data that simulates normal and fraudulent transaction behaviors. This synthetic data is used to train fraud detection models, making them more robust and adaptable to new tactics used by fraudsters. When it flags a potentially fraudulent transaction or behavior, it triggers an alert to human analysts or automated systems for further investigation. For flagged transactions, GenAI provides insights and context to human analysts, helping analysts make informed decisions swiftly. It can even automatically approve low-risk transactions, reducing the cycle time and enhancing productivity. Human analysts' decisions and feedback are incorporated into the GenAI's learning process to strengthen the performance and keep it aligned with the latest fraud detection strategies. 

4-      Accelerate back-office automation to Hyper-automation:

GenAI can play a crucial role in enhancing the capabilities of automation tools like Robotic Process Automation (RPA) and Optical Character Recognition (OCR). It can help address exceptions and failures that RPA bots encounter, reducing the need for manual intervention and improving the overall efficiency of automated processes. RPA bots follow predefined rules, but they often encounter exceptions that fall outside these rules. GenAI can be employed to analyze these exceptions, understand the context, and generate appropriate responses or solutions. For complex exceptions, GenAI can analyze the failure data and generate human-readable explanations, helping IT teams or business users understand why the exception occurred. Similarly, GenAI can analyze and correct OCR errors by comparing the extracted text with known patterns, historical data, and contextual information.

GenAI can continuously learn from exceptions and failures over time. As it encounters more cases, it becomes better at identifying patterns and predicting potential exceptions. This real-time learning enables GenAI in handling more and more complex exceptions with time.

By combining GenAI with RPA and OCR, Banks can achieve a higher level of automation maturity. This synergy enables hyper-automation, reduces manual intervention, and enhances the efficiency and accuracy of processes, ultimately leading to improved customer experiences and operational excellence.

 

The integration of GenAI into banking back-office operations represents a significant leap forward in the industry's digital transformation journey. Banks stand to reap substantial benefit from enhancing decision-making and improving operational efficiency to bolstering security measures and paving the way for hyper-automation. As the financial landscape continues to evolve, embracing GenAI technologies will be paramount for banks to stay competitive, agile, and resilient in an ever-changing market environment. 

References:

Economic potential of generative AI | McKinsey

CB Insights Report The State of Generative AI

 

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