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AI in Banking and Finance

Artificial Intelligence is an integral tool in the banking and finance industry and reshaping the way the institution is improving customer service, fraud detection, and lending services. The technology is making banking processes not only faster but safer and operations more efficient.

According to BuiltIn, AI in banking industry is expected to keep growing and it is projected to reach $64.03 billion by 2030. From a recent Deloitte survey of IT and line-of-business executives, 86% of financial services AI adopters say that AI will be very or critically important to their business’ success in the next two years.

A report by Business Insider suggests that nearly 80% of banks are aware of the potential benefits that AI presents to their sector. Another report suggests that by 2023, banks are projected to save $447 billion by using AI apps. These numbers indicate that the banking and finance sector is swiftly moving towards AI to improve efficiency, service, productivity, and reduce costs. AI has integrated itself in all levels of banking operation – front, middle and back.

The Rise of FinTech

Bank use Fintech or Financial Technology for back-end-processes and monitoring of accounts and activities “behind-the-scene” of consumer-facing solutions like your bank’s mobile app or website to check your account balances, deposit checks or transfer funds. Individuals use fintech to access various bank services including payment non-contact payment options through smartphones.

 

The rise of FinTech has proven the unbounded potential of AI and ML application in banking and finance. This can be through AI-powered real-time biometrics and facial recognition when opening accounts remotely using bank apps. The platform is used to scan identification documents to verify identity and also use the same information when verifying transactions.

Use Cases

Cyber-threat and fraud detection

Advancement in technology means more sophisticated tools and techniques for cyber-threat and fraud and these activities are dominant threats in the banking and finance industries affecting the institution and its consumers, the public. 

 

With AI in banking and finance, fraudulent activities can be detected in real-time, threats addressed, and the safety of financial assets is enhanced.

One of the main benefits of AI-powered fraud detection systems is their ability to identify patterns and anomalies in transaction data. These systems can use machine learning algorithms to analyse transaction data and identify patterns that are indicative of fraudulent activity, such as unusual purchase amounts or locations. The algorithms can also learn from past fraudulent activity to improve their accuracy over time.

Another benefit of AI-powered fraud detection systems is their ability to monitor customer behavior for suspicious activity. These systems can use machine learning algorithms to analyse customer data, such as login times, IP addresses, and device types, to identify patterns that are indicative of fraudulent behavior. The systems can also use biometric data, such as facial recognition or voice recognition, to verify the identity of customers and prevent identity theft.

AI-powered fraud detection systems can also provide real-time alerts to bank staff, enabling them to quickly respond to fraudulent activity. For example, if a system detects a fraudulent transaction, it can send an alert to a bank’s fraud department, which can then take action to prevent further fraudulent activity.

Credit Analysis and Dynamic Risk-Based Pricing for Loans

Credit analysis and dynamic risk-based pricing for loans that evaluate a borrower’s creditworthiness and determine appropriate interest rates. AI can help improve the accuracy of credit analysis and enable dynamic risk-based pricing, which can benefit both the financial institution and the borrower.

 

Credit analysis using AI involves analysing a vast amount of data, including credit scores, payment history, income, employment history, and other factors that may impact a borrower’s ability to repay a loan. By analysing this data, AI algorithms can create a more accurate picture of a borrower’s creditworthiness and determine the likelihood of loan default.

 

Dynamic risk-based pricing is a pricing strategy that allows financial institutions to set loan interest rates based on the borrower’s risk profile. By using AI to analyse data, financial institutions can offer lower interest rates to borrowers with a lower risk of default, while charging higher rates to borrowers who are deemed to be at a higher risk. This approach can help financial institutions to reduce losses due to loan defaults and can also enable them to attract borrowers with lower credit scores who may have previously been denied loans.

Customer Experience and Growth

AI helps banks deliver better customer experience and drive growth by having the ability to offer more personalised and efficient services to their customers. 

 

Service improvements and customer service is among the areas in service which has greatly improved because of AI.  AI-powered Chatbots and Virtual Assistants provide 24/7 customer service, answering common questions and providing assistance with simple tasks. This reduces wait times and improves customer satisfaction.

 

AI algorithms can analyse customer data to identify patterns and preferences, allowing banks to offer personalised services to each customer. For example, AI can recommend customised financial products, suggest investment opportunities, and provide targeted marketing campaigns that are more likely to resonate with each customer.

AI-Enabled Future: Integrating AI in Banking and Finance

Develop an AI Strategy

The enterprise must develop and AI strategy aligned with the organisation’s priorities, goals, and values. It’s crucial to identify the gaps and pain points which AI can help address and problems it can help resolve. This can be in the area of people and processes. Financial institutions must always keep in mind that their AI strategies must always comply with industry regulations and standards.

 

An important part of AI strategy development is the formulation of policies and internal processes related to data, infrastructure and talent to provide clear guidelines in the organisation’s AI adoption.

Identify and Define Use Case-driven Processes

For AI in banking to be effective, it must address the highest-value opportunities for the enterprise. Banks must evaluate how AI can provide a solution to their current operational processes.

 

After identifying potential ML and AI use cases, proof of concept and feasibility must be looked into.

Drive Sustainable Outcomes

AI and ML enables financial institution to scale up as needed to address the changing landscape of their requirements from data sources, amount of data, and continue to develop to provide solution to new challenges. Making the technology relevant and instrumental in driving innovation and efficiency.

To learn more about these use cases, and to know more about how AI in bankin and finance can be integrated into your operation, contact AI Consulting Group. We have an experienced team of experts that can collaborate with you from planning, design, execution, and provide support. 

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