For the finance industry, a recovery from the impact of the pandemic will enable initial testing with artificial intelligence and machine learning to become more of a normality. The implications of the pandemic required significant adaptation and many financial businesses have needed to make major transformations to meet customer expectations and ensure their core operations and procedures continue to run smoothly. This has spurred a wider interest in AI and ML technology, systems that minimise the need for manual processes, improve overall security and provides additional time for other innovative tasks. By reducing the time spent in creating an idea and generating value for a business, AI and ML have the potential to provide long-term advantages for businesses.
People’s expectations of financial services have increased and as a result, we are seeing banks transform into digitally-focused platforms, similar to technology leaders, with the capabilities to enhance their customer focus. The question is how can banks and the wider financial services industry utilise AI to its fullest potential.
Many financial services companies have already implemented AI and ML systems before the pandemic escalated. However, many organisations have experienced challenges in understanding how certain features of AI can be of benefit, and as a result, businesses didn’t necessarily yield the expected results from AI. Analysts believe this will improve over the coming months, with predictions that AI and ML deployment will become a major part of the economic recovery from the pandemic and Covid-19 has emphasised certain areas where AI should be implemented. This includes credit decisions, fraud prevention and improving the overall customer experience.
What financial services processes can be enhanced by AI?
Using automation to enhance document processing
Robotic process automation can enhance several functions, overall efficiency, speed and the accuracy of vital financial processes, resulting in considerable cost savings. One particular area that has emerged is referred to as ‘electronic know-your-customer’, a remote paperless solution that eliminates the costs associated with certain protocols such as client verification and signatures. A process that was commonly time-consuming and repetitive has been improved with more organisations embracing intelligent automation systems that are capable of managing and extracting unstructured data and other information.
Operating an NLP system (natural language processing) enabling the identification of missing data, means platforms provide highly accurate and reliable information. Overall handling time is reduced and businesses gain a competitive advantage by enhancing the overall customer experience.
Improving the efficiency of customer support
Virtual assistants are capable of responding to the needs of customers with little input from employees. The time and effort applied to inbound enquiries are greatly reduced, enabling more time for employees to focus on long-term projects that will generate further innovation and success in the business. Chatbots and other systems will inevitably become more common in the finance industry, with major businesses like JP Morgan implementing bots to enhance their overall operations and improve customer support.
Implementing analytics into risk management
Even being equipped with the right data, measuring and predicting credit and risk management processes is challenging. This is largely down to certain individuals and businesses not disclosing their ability to pay back loans. To manage this, businesses are applying AI for risk assessment, to understand the creditworthiness of individuals and businesses. Credit companies like Equifax use a combination of AI, ML and other analytical systems to measure various sources and evaluate the overall risk level and generate key insights into their customers.
Previously, lenders would rely on limited data sets, such as salaries and credit scores. AI enables businesses to consider a much broader digital footprint of customers to determine overall credit risk.
How businesses and clients interact has changed significantly in the last year, and this applies to the finance sector. Before the pandemic took effect, businesses were merely testing the waters with new technology. However, the widespread adoption of AI and ML over the last year has been spurred from the need to innovate and improve overall resilience.
The finance industry is now well aware of the benefits of AI. The early stages of a transformative process towards AI that began before the pandemic has accelerated and is quickly establishing itself as a standard in finance.