Use Cases of Artificial Intelligence in Banking

Traditional reliance on credit history, scores, and references is being complemented by AI’s ability to analyze customer behavior and transaction patterns. These systems can even proactively warn banks about risky behaviors that might lead to loan defaults, changing the landscape of consumer lending. At Syndell, we work to develop unique AI and ML-based models that help the banking and finance industry improve revenue, cut costs, and manage risks.

This gives customer service representatives more time to handle complicated inquiries. It is crucial to maintain the bank’s reputation, to withhold customer trust, attract new business, or pull potential investors is not an easy thing. Negative public opinions, adverse media coverage, or poor customer experiences when aligned cause reputational risk which directly impacts customer retention, business relationships, and overall profitability. When customers apply for loans, ChatGPT can provide real-time guidance and support throughout the process. A popular example of this AI use case in banking is Mastercard, which introduced Decision Intelligence as one of its top security techniques.

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This value creation is driven by AI’s ability to personalize services for customers, resulting in increased revenues. Moreover, AI-driven automation reduces operational costs, minimizes error rates, and optimizes resource utilization, further contributing to value generation. AI technology is transforming the banking industry, providing customers with improved experiences and convenience. It reduces the time taken to record KYC information, eliminates errors, and automates eligibility for facilities like personal loans and credit. AI-based software can reduce approval times for loan disbursement and accurately capture client information to set up accounts without any error, ensuring a smooth experience for customers.

Conversational AI in financial services is also playing a significant role in algorithmic trading. Virtual assistants equipped with AI capabilities can process natural language queries from traders, provide real-time market insights, analyze trading strategies, and execute trades based on predefined parameters. AI-powered algorithms can analyze vast amounts of market data, including historical price trends, market indicators, and news sentiment, to identify patterns and predict market movements. This allows financial institutions to execute trades with precision and efficiency.

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In terms of hard numbers, the AI in Education Market reached an impressive $4 billion in 2022. Even more noteworthy, it’s expected to grow at a steady rate of over 10% per year from 2023 to 2028. In fact, there are ways to predict and monitor its effects to make agricultural practices more efficient and reduce unnecessary costs. You might find it unsettling to know that cyber-attacks are more common than you’d think—a new attack occurs approximately every 39 seconds. In fact, back in 2019, nearly a third of all threats targeting the financial sector were cyber-related, making it one of the sectors most vulnerable to such attacks, second only to healthcare.

It provides customized asset allocation strategies based on customer profiles. Utilizing this comprehensive understanding, AI engages in a dialogue with customers to establish clear financial objectives. With the ability to adapt and learn, AI formulates personalized asset allocation strategies to optimize each customer’s unique financial position, ensuring a tailored and informed approach to wealth management. Overall, the use of AI in banking and financial services is still in its early stages, and there are many opportunities for further innovation and growth. As AI continues to evolve, we expect to see even more advanced applications in investment management, loan underwriting, and compliance.

For example, an AI chatbot could ask users to answer a security question or perform a multi-factor authentication (MFA). However, these can be costly to run and maintain, and in some cases, they aren’t very effective. Another example of process automation with AI is the ability to verify personal ID.

They can outsource or collaborate with a technology provider if they lack in-house experts. After identifying the potential AI in banking use cases, the QA team should run checks for testing feasibility. The next step involves identifying the highest-value AI opportunities, aligning with the bank’s processes and strategies. Artificial intelligence (AI) is the discipline of automated decision-making and the execution of actions by using computer systems and applying mathematical techniques to large amounts of data. Despite the power of the human mind, there are many processes and analyses in which humans are not highly efficient. This is usually the case when there is a high mechanical component to the problem or when we depend on finding a pattern in large volumes of data.

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They can be used for sending push notifications regarding transactions in real-time, informing customers about their account balance for better budget management, and sending updates on credit bill payments and all. Utilized by top banks in the United States, f5 provides security solutions that help financial services mitigate a variety of issues. The company offers solutions for safeguarding data, digital transformation, GRC and fraud management as well as open banking. An AI-powered search engine for the finance industry, AlphaSense serves clients like banks, investment firms and Fortune 500 companies. The platform utilizes natural language processing to analyze keyword searches within filings, transcripts, research and news to discover changes and trends in financial markets.

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