The impact of artificial intelligence on the financial sector, as well as scalable AI solutions, has the potential to revolutionize the finance sector and provide better customer-centric services while mitigating potential cyberattacks.
By 2023, the integration of AI in banking and finance is projected to enable corporations and banks to save $447 billion. AI allows machines to perform human-centric tasks and learn from their experiences, thereby automating repetitive jobs and freeing up employees’ time to concentrate on more creative and strategic projects.
AI is paying rich dividends by enabling new ways to meet customer demands for smarter, easier, and safer ways to access, spend, save, and invest money. The financial industry is progressively adopting AI-based technologies leveraging their potential. AI has been the driving force behind emerging technologies such as blockchain, big data analytics, robotics, IoT, and RPA.
Rapidly indexing and learning from gargantuan amounts of data created by humans, AI systems perform intelligent searches, interpreting both text and images to discover patterns in complex data and large language models to then act on those learnings.
The AI market is growing rapidly. The market size was valued to be $328.34 billion in 2021, it is expected to reach $1394.30 billion by 2029 at a CAGR of 20.1%.
Benefits of AI in the Finance & Banking Sector
The number of banking and financial institutions using AI is on the rise, as AI can process enormous amounts of data quickly and accurately, identify trends and patterns, detect anomalies, make predictions, and provide recommendations. Additionally, AI can help reduce costs associated with fraud detection and prevention, customer service, and compliance. AI-enabled chatbots can provide 24/7 customer support at a fraction of the cost of human customer service representatives. By automating repetitive tasks, AI can free up employees’ time helping them focus on more creative strategic tasks. As the banking and finance industry continues to evolve, AI will play a key role in the transformation.
Looking at 2023 and Beyond
The use of AI in finance has grown significantly in recent years. Here are some use cases of how BFSI (Banking, Financial Services, and Insurance) institutions are using AI:
- Generative AI: The sensation of ChatGPT which displayed the power of LLMs (large language models), and generative AI has seen diverse use cases from story writing to writing software code. Banks might consider applying it in drafting personalized emails to their customers, helping software engineers write better codes, and creating better upselling and cross-selling messages for marketing personnel.
- Customer Service and Chatbots: AI-powered chatbots are being used by BFSI institutions to provide customer service and support. Chatbots can handle routine inquiries, provide assistance with transactions, and offer personalized recommendations based on the customer’s history.
- Risk Management: BFSI institutions are using AI to manage risks and assess creditworthiness. AI-powered algorithms can analyze credit scores, payment history, and other factors to determine the likelihood of default and manage credit risk.
- Wealth Management: AI is being used by BFSI institutions to provide personalized investment recommendations to clients. Machine learning algorithms can analyze historical data and market trends to identify investment opportunities and offer personalized investment advice.
- Trading and Portfolio Management: BFSI institutions are using AI to optimize trading strategies and portfolio management. AI-powered systems can analyze large volumes of data, including market trends and news articles, to identify potential investment opportunities and optimize portfolios.
- Compliance and Regulatory Reporting: BFSI institutions are using AI to ensure compliance with regulations and streamline regulatory reporting. AI-powered systems can analyze large volumes of data and generate reports that comply with regulatory requirements.
- Tech-powered ESGs: Environmental, Social, and Governance (ESG) goals are being embraced and embedded as KPIs across the Financial Services and Insurance (FSI) industry; more companies are adopting AI solutions to help achieve their targets. For instance, AI can empower FSIs to navigate through a wide range of ESG signals and translate them into usable insights, as well as to tussle and process unstructured data. While the challenges remain in getting clarity on those ‘goals’ and the ‘signals,’ the good news is – most corporate leaders are taking up the mantle of ESG as corporate policy. In 2023 we will be witnessing this policy trickle down into industry culture, thanks to AI.
- Fraud Detection: Have you ever received a call from your credit card company after you have made a few purchases? Through AI, fraud detection systems analyze a person’s purchasing behavior and send out an alert if it detects any unusual or abnormal spending patterns. AI and ML improve compliance and workflow and identify fraudulent and unusual financial transactional behavior. AI helps reduce your operational costs by limiting vulnerability to fraudulent documents. For instance, credit card fraud prevention, identity theft, fraud detection in AML (Anti-Money Laundering), and other illicit financial activities.
- Risk Management: Natural language processing (NLP) is leveraged to quickly scan a large volume of legal and regulatory texts for assessing and resolving compliance issues. Here AI and NLP can process thousands of documents without any human involvement via data analysis and data classification to get key information. AI and NLP help in the identification of fake reviews, sorting out spam, and phishing mail filter, validates if data info is fake or true. Of late, banks are turning to accelerated computing to speed price discovery, risk calculations, and backtesting which prove crucial in this volatile financial climate where more accurate data and real-time risk calculations are very much needed. Widely used by capital markets and hedge funds, financial institutes need to adopt AI and accelerated computer-driven simulated techniques to stay in the race. Overall, ML can help financial experts use data in pinpointing trends, identify risks, minimize and improve workforce efficiency, and ensure better information for future planning.
- Customer Experience: AI will push banks to create greater products that meet basic client demands with the least amount of people involvement, right from providing info on savings suggestions to expenditure analysis. AI can furnish banks with information on goods and services, profit margins, and expenses. The applications include – the categorization of transactions. Even in credit scoring, prospects can receive a loan in 15 minutes instead of hours or days. While the recommender engine proposes the next product a customer might be interested in by cross-selling and upselling products or services.
- Process Automation: There has been an increased use of mobile assistant apps and AI money chatbots which keep an eye on your accounts. To help achieve your individual financial goals, and determine your target savings or consumption rates, your digital finance assistant will manage the rest, further, it provides advice and recommendation on how to meet your financial goals. AI-powered assistants leverage natural language to develop chatbots. Widely helpful in document reading to extract valuable information and in the classification of documents, it also analyzes top market trends and performs forecast analysis automatically for specified areas.
- Trading: As AI is known to analyze large data sets and underlying patterns, it is not surprising that financial analysis, algorithmic trading, and other investment strategies will hugely benefit by using ML and NLP, and AI techniques. Algorithmic trading is done by capturing sentiment analysis of online information such as user tweets, articles, and news supporting financial advisory and portfolio management.
- Data Bias and Quality Analysis: AI provides great scope for enhancing data quality and extracting insightful information. While the uses of Ai and ML can help in the following areas – Locating inconsistencies and discrepancies in customer data (e.g., addresses and countries) by increasing the data quality and identifying suspicious or inaccurate customer data.
- Preventing Cyberattacks: Customers expect banks and other financial institutions to keep their money and personal information safe and secure. Here, AI can help customers with just that. It is said that up to 95% of cloud breaches are caused by human error. AI can boost company security by analyzing and determining normal data patterns and trends and checking for any discrepancies, irregularities, or unusual activity by sending alerts.
Overall, the financial sector has always recognized the potential disrupting benefits of using AI-based solutions. Customers now prefer digital tools and applications transcending from traditional banking channels to virtual AI-based services.
AI and BFSI – The Future
Moving forward, the focus would be more on gaining new clients by providing individualized banking experiences to their customers. While digital services will be completely automated including risk assessment and prevention of cyber-attacks and defending sensitive data from hackers would be the prime focus. Financial institutions would focus on customers’ needs by delivering customer-centric services -enabled by AI, ML, and NLP becoming very essential in 2023, a year that has witnessed tough times of war, natural calamities, and economic crisis.
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