March 18, 2023
5 minutes to read
By Cogito Tech LLC.
Despite the long-term technology dependence and data-intensive nature of the banking sector, data-driven artificial intelligence (AI) technology can offer a faster and more efficient way to make banking and financial services easier and more efficient. It is now recognized that the use of artificial intelligence in Finance and Banking can help increase productivity, create a growth agenda, enhance differentiation, manage risk and regulatory requirements, and positively impact the customer experience.
Until recently, the development of complex banking systems based on artificial intelligence was expensive, limiting their implementation in the banking and financial sector. As AI and machine learning (ML) technologies advance along with improvements in data annotation and labeling processes, banks and financial institutions are finding it easier to integrate AI technologies into their systems and day-to-day operations.
Implementation of AI in finance and banking
Throughout the fintech industry, AI is quickly finding its way into every corner and is expected to impact how businesses and consumers make financial decisions on a daily basis. From payments to lending, investments to insurance, AI is impacting every part of fintech. Artificial intelligence models are likely to soon replace humans in a variety of tasks, including borrower placement, corporate expense approval, payment fraud detection, and pricing of complex insurance products.
Future developments in AI in finance and banking will provide a number of opportunities to integrate them into existing and new systems, covering a wide range of applications such as credit assessment, risk management, portfolio optimization, financial health management, government service management and engagement. customers. To reduce or eliminate the need for upfront capital expenditure to deploy, scale and implement AI solutions, banking and financial organizations are adopting new architectures based on new age technologies.
The following 5-point analysis shows how data-driven AI can be used in banking to add value to banking operations, from revenue growth in the front office to operational efficiency 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. Call centers often hire additional staff during times of high call volume. However, banks should use artificial intelligence technologies to handle fluctuations in call volume. A conversational AI agent can conduct personalized conversations based on a variety of information sources, including customer records, social media, the current economic outlook, historical customer information, and information about call center patterns.
Banks and financial institutions spend significant amounts of money on customer service. As a result, any savings from reduced support ticket volumes, time, and costs through AI can have a positive impact on their bottom line. In consumer banking, a growing number of banks are using advanced artificial intelligence agents (in particular, conversational agents), which enable them to answer hundreds of common questions and learn to answer additional queries when interacting with customers, resulting in cost reductions. , improved consistency and scalability, and improved performance.
2. Debt collection and recovery
Banks must adjust their reach, especially in uncertain economic times, to increase recovery rates for delinquent customer accounts. Customers are delinquent for a variety of reasons, including job losses, unpaid bills due to lack of reminders, address changes, and collections. It is possible for artificial intelligence to increase efficiency and develop predictive strategies that can benefit both consumers and lenders.
Using customer data can enable banks to identify early warning signs of delinquency and default, predict why customers might miss payments, and offer tailored solutions to help them catch up. Banks can streamline the debt recovery process by using AI-based debt collection assistance such as machine learning to communicate with customers based on their behavior.
3. Risk assessment and compliance
The roles of intermediaries have historically been defined as evaluating 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 can lead to higher prices and limited availability. With the help of artificial intelligence and machine learning, lenders, insurance companies, payment providers and ultimately investors can better assess risk, which, if applied correctly, will enable historically underserved groups to gain access and lower fees, thereby accelerating economic growth. :
Complying with government rules and regulations requires banks to spend a lot of money. To streamline labor-intensive compliance processes and stay compliant with regulatory changes, banks can use 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 reporting systems, deep learning techniques and natural language processing can reduce implementation times as well as lead times.
4. Streamlined placement process
Underwriting processes can be accelerated and risk assessment improved through robotic process automation, machine learning models and a variety of data sources. It may be possible to speed up this process by automating the scanning of documents and the manual procedures associated with collecting the relevant information. Machine learning models capable of analyzing data from various sources (such as social media posts and third-party data) can be used to accurately assess borrower risks and speed up loan approvals.
Recently launched a digital line of credit for sellers offered by a major retailer. A digital line of credit is offered using information from authorized sellers (such as sales volume and revenue) to identify potential applicants. As a result, the partner bank can offer lines of credit to borrowers who meet its underwriting criteria and speed up the loan approval process. Compared to the standard approval time of seven days or more, the process is automated, reducing the loan approval time to two days.
5. Personalization of the customer experience
More than 50% of bank customers say personalized service is a key factor in keeping them loyal to their banks, although only 35% of traditional banks offer personalization that meets customer needs. It is therefore imperative that banks invest more than ever to personalize the services offered to their customers, leading to greater customer loyalty and trust. Micro-segmentation of customers and prospects with the help of data-driven AI should be the norm for banks. Banks can more accurately predict customer and prospect needs and behavior using this level of granularity.
Good customer relations are very important in many industries, especially in financial services. Trust, compassion, and warmth all fall into this category. In addition to being able to accurately complete tasks and transactions on your behalf, next-generation conversational agents can provide engaging, sympathetic and responsive dialogue with customers. To enable these conversational social bots in custom applications, the industry must learn how to build and use AI and NLP-based models for personalized customer experiences.
A 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 procure loans in a cost-effective manner, allowing them to reach underserved populations at a lower marginal cost. Artificial intelligence (AI) has the potential to change the way banking and financial operations operate, particularly by improving customer experience, increasing efficiency, increasing security and reducing costs.