AI in Finance – 5 Benefits for Better Banking

March 18, 2023 5 min read By Cogito Tech. 1182 views

Despite the long-standing technology dependence and the data-intensive nature of the banking sector, data-enabled artificial intelligence (AI) technology can offer a faster and more efficient way to drive ease and efficiency of transacting banking and financial services. Now it is well known that employing AI in finance & banking can contribute to improving productivity, enabling a growth agenda, enhancing differentiation, managing risks and regulatory requirements, and impacting the customer experience positively.

Until recently, sophisticated AI-backed banking systems were expensive to develop, restricting their implementation in the banking and financial industry. As AI and machine learning (ML) technologies progress along with improvements in data annotations and labeling processes, banks and financial institutions find it easier to integrate AI technologies within their systems and daily operations.

Embedding AI in Finance & Banking

Throughout the fintech industry, AI is rapidly finding its way into every corner and is expected to influence how businesses and consumers make financial decisions on an everyday basis. From payments to lending to investing to insurance, AI is affecting every part of fintech. Artificial intelligence models are likely to soon replace humans in a number of tasks, including the underwriting of borrowers, the approval of corporate expenses, the detection of payment fraud, and the pricing of complex insurance products.

Future developments in AI in finance & banking will provide a number of opportunities for integrating them into existing and new systems, covering a wide range of applications such as assessing creditworthiness, managing risks, optimizing portfolios, managing financial health, administering government services, and engaging customers. In order to reduce or eliminate the need for up-front capital expenditures to deploy, scale, and implement artificial intelligence solutions, banking & financial organizations are adopting new architectures based on new-age technologies.

The following 5-point analysis outlines how data-driven AI can be utilized within a banking organization to generate added value to banking operations—from revenue growth in the front office to operational efficiencies in the back office:

1. Improved Customer Service

AI can be used to improve operational efficiency in areas such as customer call routing and hold time calculation. In times of high call volume, call centers frequently hire supplemental staff. However, banks should implement artificial intelligence technologies to handle fluctuations in call volume. The conversational AI agent has the ability to engage in personalized conversations based on a range of information sources, including customer records, social media, a current economic outlook, historical information about the customer, and information about call center patterns.

A significant amount of money is spent by banks and financial institutions on customer service. As a result, any savings resulting from reduced support ticket volumes, time, and costs through artificial intelligence could have a positive impact on their bottom line. In the consumer banking industry, a growing number of banks are utilizing advanced artificial intelligence agents (conversational agents in particular), enabling them to answer hundreds of common questions and learn to answer additional queries as they interact with customers, resulting in a reduction in expenditures, improved consistency and scalability, and improved efficiency.

2. Debt Collection & Recovery

Banks need to tailor their outreach, especially during uncertain economic times, in order to increase the recovery rates of delinquent customer accounts. Customers are delinquent for various reasons, including job losses, missed payments because of a lack of reminders, changes of address, and collections. It is possible for artificial intelligence to enhance efficiency and develop predictive strategies that can benefit both consumers and lenders.

The use of customer data can provide banks with an opportunity to identify warning signals for delinquencies and defaults, predict the reasons why customers might miss payments, and offer tailored solutions to help them catch up on payments. Banks can streamline the process of debt recovery by utilizing AI-driven debt collection assistance, such as using machine learning to communicate with customers in a way that is based on their behaviors.

3. Risk Assessment and Mantaining Compliances

The roles of intermediaries have historically been defined as assessing and pricing risk using imprecise models, high-level data, and human judgment to facilitate transactions. There is a risk of bias and inaccuracy in this process, which may result in higher prices and limited availability. With the help of artificial intelligence and machine learning, lenders, insurance companies, payments providers, and eventually investors can better assess risk, which, when applied properly, can enable historically underserved groups to gain access and reduces intermediary fees, thereby accelerating economic growth.

Compliance with government rules and regulations requires banks to spend a lot of money. In order to streamline labor-intensive compliance processes and maintain compliance with regulatory changes, banks can leverage artificial intelligence to optimize efficiency and save money. By reading compliance requirements from regulatory websites, notifying banks of updates, and automatically incorporating those changes into the report generation systems, deep learning techniques and natural language processing can reduce implementation timeframes, as well as reduce implementation timeframes.

4. Streamlined Underwriting Process

Underwriting processes can be accelerated and risk assessment improved with robotic process automation, machine learning models, and a variety of data sources. It may be possible to expedite this process through the automation of the scanning of documents and the manual procedures involved in gathering relevant information. In order to accurately assess borrowers’ risk and expedite loan approvals, it is possible to use machine learning models that are able to analyze data from various sources (such as social media posts and third-party data).

There has recently been the launch of a digital credit line for sellers offered by a large retailer. A digital credit line is offered using information from authorized sellers (such as sales volume and revenue) to identify potential applicants. As a result, a partner bank is able to offer credit lines to borrowers who meet its underwriting criteria and expedites the loan approval process. Compared to the standard approval time of seven days or more, the process is automated, which reduces the time it takes to approve a loan to two days.

5. Personalizing Customer Experience

Over 50% of bank customers say personalized services are a key factor in keeping them loyal to their banks, even though only 35% of traditional banks offer personalization that is appropriate to meet customers’ needs. It is therefore imperative that banks make greater investments than ever in order to personalize the services they offer to their customers, resulting in greater customer loyalty and trust. Micro-segmenting customers and prospects with the help of data-driven AI must be the norm for banks. Banks can predict customer and prospect needs and behaviors more accurately by using this level of granularity.

Having a good customer relationship is crucial in many industries, especially financial services. Trust, empathy, and warmth all fall under this category. In addition to being able to accurately perform tasks and transactions on your behalf, future-generation dialog agents can provide engaging, sympathetic, and responsive dialogue to customers. In order to enable these conversational social bots in specific applications, the industry will have to learn how to build and make use of AI and NLP-based models for personalized customer experience.

Final Thought

The application of AI in financial services can bend the cost curve for a number of banking and financial operations. along the value chain. Businesses can use AI to monitor fraud, comply with regulatory requirements, and underwrite credit in a cost-effective manner, enabling them to reach underserved populations at a lower marginal cost. Artificial intelligence (AI) holds the potential to transform the way banking and finance operations function — particularly by improving customer experience, increasing efficiency, enhancing security, and reducing costs.

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