Introduction
AI is increasingly becoming a cornerstone in business and enterprise banking, offering transformative benefits across various functions. From automating routine tasks and enhancing customer service to optimizing financial analysis and risk management, AI technologies are driving efficiency and innovation. AI-powered tools enable more accurate credit scoring, streamline compliance processes, proactively manage risk, and provide insightful analytics for strategic decision-making. According to a report by ResearchandMarkets, “the global AI in banking market size was valued at $3.88 billion in 2020, and is projected to reach $64.03 billion by 2030, growing at a CAGR of 32.6% from 2021 to 2030”. The use cases given below highlight the numerous ways in which AI improves Business and Enterprise Banking.
Use Cases
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1. Identify money laundering activities
AI systems are employed to detect money laundering activities more accurately, reducing false positives. They also help in identifying potential money laundering activities by customers, enhancing overall financial security.
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2. Convert and analyze P&L documents
Converting profit and loss documents from different formats (e.g., PDF, scanned, XBRL) into a standardized electronic format and analyzing financial statements for risk-adjusted returns. This process, applicable to both public and private companies, enables lenders to expedite loan approvals.
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3. Convert and analyze commercial real estate P&L documents
Converting commercial real estate profit and loss documents (rent rolls) from different formats (e.g., PDF, scanned, XBRL) into a standardized electronic format and analyzing financial statements for risk-adjusted returns. This rapid and accurate conversion helps lenders approve loans more quickly.
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4. Extract key-value pairs from financial documents
Extracting key-value pairs and relevant attributes from financial documents such as memoranda of understanding, letters of credit, guarantees, performance bonds, and syndicated loans. These extracted data points are then used for accurate reconciliation processes.
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5. Acquire data related to market sentiments and key performance indicators
AI systems, particularly LLMs and GPTs, are utilized to efficiently gather external data to understand market sentiments and key performance indicators of financial markets and individual companies. This real-time intelligence aids financial institutions in better balancing their risk-reward systems.
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6. Develop Decision Support Systems
AI systems are used to develop Decision Support Systems in capital markets, brokerage, retirement, and wealth management. These systems predict ratings for small and medium-sized companies that analysts cannot cover due to time constraints and scrape data from news media, 10Ks, 10Qs, equities research, and fixed-income documents.
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7. Identify potential clients
AI recommender systems assist investment bankers in identifying potential clients more effectively. They also provide accurate information on IPOs, M&A deals, and large credit loans that are worth pursuing, enhancing decision-making processes.
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8. Retrieve relevant internal information
Investment banking professionals utilize Large Language Models and semantic search to query internal information available in various formats such as paper documents, presentations, databases, emails, and folders. This helps in efficiently retrieving relevant information for decision-making.
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9. Proactive risk management
AI is used to assess business risk concerning vendors or banking clients who might go bankrupt due to adverse market conditions or high client concentration. This helps in proactive risk management and mitigation.
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10. Forensic accounting research
AI systems aid banks and financial institutions in forensic accounting research by training on historical data containing both anomalous and non-anomalous transactions. This training helps in making accurate inferences for new data, enhancing fraud detection and financial analysis.
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11. Improve customer service quality
LLMs can be used to train call center agents and ensure that scripts and necessary disclosures are followed during inbound and outbound calls. Additionally, AI systems can analyze the tone and sentiment of both business customers and call center agents during calls, improving customer service quality.
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12. Predict sales offers
Existing Machine Learning (ML) tools are effective in predicting marketing or sales offers for specific business customer segments based on available parameters. Consequently, AI systems can automate these sales offers and provide AI-based advising, enhancing marketing efficiency.
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13. Generate personal messaging
AI systems, such as LLMs and GPTs, assist in the creative process of generating one-to-one personalized messaging at scale using conversational language. This helps improve customer experience, retention, and cross-sales by making interactions more engaging and relevant.
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14. Client information management
Collect business client data and documents through a web-based digital workflow. This process ensures data accuracy and completeness in real-time, facilitating efficient and accurate client information management.
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15. Fraud detection and reputation management
Process scans or pictures of certificates of incorporation, bills, contracts, or IDs into systems using management order fulfillment and risk recognition. This enables real-time prediction of money laundering activities and assessment of potential credit risks, enhancing fraud detection and reputation management.
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16. Enhance client experience and onboarding process
Reconcile business client data with reference data sources such as historical background data, police checks, or tax databases. Use chatbots to support and guide clients through the onboarding process and assist in selecting appropriate products, enhancing client experience and compliance.
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17. Efficient onboarding experience for clients
Support the overall onboarding process management through a digitalized workflow that includes task routing, prioritization, monitoring, and escalation of exceptions. This ensures a smooth and efficient onboarding experience for clients.
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18. Enhance business client profiles
Enrich business client profiles by utilizing data available on social media and other public web sources. This provides a more comprehensive understanding of clients, aiding in personalized service and marketing strategies.
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19. Secure authorized connections
Secure business clients’ authorized connections through advanced methods such as voice recognition and other biometric authentications. This enhances security and ensures that only authorized individuals can access sensitive information.
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20. Resolve customer queries
Resolve business customer queries related to services, transactions, and account details through a self-service digital platform that leverages chatbots. This provides quick and efficient customer support, improving overall satisfaction.
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21. Predict client churn
Identify the most accurate variables predicting client churn by analyzing transactional data. Define the demographic profiles and reasons for churn, and create tailored campaigns to prevent it. This helps in retaining clients and reducing churn rates.
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22. Enhance personalized marketing strategies
Use business customer demographic data to define granular transactional patterns and customize product offerings and promotions. Identify the next product to buy based on customers’ demographics, services, product portfolio, transactional behavior, and contact history, enhancing personalized marketing strategies.
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23. Build partnerships
Increase the volume of new business clients, improve user traffic, and enhance client satisfaction by building ecosystems with partner companies such as car rentals, airlines, and hotels. This creates a more integrated and convenient experience for clients.
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24. Reduce capital utilization
Reduce required capital utilization by building and monitoring assets and optimizing models. Identify usage patterns, technical issues, and traffic inefficiencies through analytics to improve operational efficiency and performance.
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25. Resolve technical issues
Support the resolution of technical issues by identifying root causes and suggesting real-time remediation actions. Automate some of these actions, such as server restarts, to enhance efficiency and reduce downtime.
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26. Prevent outages
Prevent outages by implementing predictive maintenance strategies. Apply data-driven insights to monitor equipment, learn from historical information, anticipate equipment failures, and proactively address them, ensuring continuous operation and reducing downtime.
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27. Improve data validation and operational efficiency
Perform routine transactions and other back-office reporting tasks to improve data validation and operational efficiency. Reconcile statements, monitor performance, and generate reports, ensuring accurate and efficient financial management.