AI Business Strategies : Leveraging Artificial Intelligence as a Competitive Advantage by Bob Pellerin

Author:Bob Pellerin [Pellerin, Bob]
Language: eng
Format: epub
Published: 2023-06-05T22:00:00+00:00

AI Use Cases in the Financial Industry

The financial industry is one of the most significant industries in the world for the integration of Artificial Intelligence. Let’s explore some of the use cases of AI in this vertical.

Fraud Detection

AI can be used to detect fraudulent activities in real-time. The analysis of massive data streams can identify unusual patterns of activity, and flag them for human review. In the case of obvious fraud, the entire system can be automated, and only the appeals process would require human intervention. This helps financial institutions prevent fraudulent transactions and protect their customers from financial loss.

HUMAN is a security company offering cutting edge AI fraud detection and mitigation. Their algorithms can detect stock-out attacks, price manipulation, false account creation attempts, and other financial attacks. This helps banks, credit institutions, and insurance.

Customer Service

AI provides customer support and client engagement in the financial industry. AI-powered chatbots and virtual assistants can help customers with basic queries, account management, and other services, providing 24/7 support without the need for human intervention.

Automation of things like checking account balance, security checks, and basic authorizations have been around for years. What AI has done is increased the scope of self-service options dramatically. Everything from setting contingency-based trades to arranging escrow is now possible without agent intervention, which speeds up customer fulfillment and lessens the burden on support professionals.

Investment Management

Portfolio management using AI can provide better analysis and investment recommendations because AI can ingest market data at a global level. This allows the AI to identify patterns that humans might not see, and make investment decisions that are more precise and reliable.

Portfolio managers are using NLP/G engines to do industry-specific analyses and to find the traits that are reliable markers for future success. Though there’s no such thing as a sure thing, a shift of just one or two percentage points can mean a shift of billions of dollars for their clients.

Risk Management

AI can be used in risk management roles to analyze financial risks and provide recommendations for reducing exposure. AI-powered systems can analyze data from multiple sources, pinpoint risk factors, suggest mitigation, and provide real-time insights for better decision making.

Perhaps one of the earliest adopters of AI risk management as it applies to the financial services fields is big consultancy. Deloitte, PWC, and KPMG all have aggressive AI analysis tools that they use for both auditing and exposure testing.

Credit Scoring

AI can be used to provide more accurate credit scoring. AI-powered systems can analyze massive amounts of data from multiple sources, including social media and other digital footprints, to provide more accurate credit scores, reducing the risk of fraud and increasing the accuracy of lending decisions.

H2O AI is just one of the firms that have designed neural network AIs that improve the credit scoring process for clients. Credit score model building for new financial products now takes days instead of months. Tighter acceptance windows saved clients tens of millions of dollars. And the robust reporting features save hundreds of hours of work per human underwriter.

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