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Traditional risk managers are now heading towards a new makeover by adding Artificial Intelligence (AI) capabilities to their rule-based fraud management systems.
FREMONT, CA: Artificial Intelligence (AI) is one of the key drivers transforming the banking industry. Financial organizations from a retail bank to global financial institutions are integrating AI to save time, boost revenue, identify risks and fraud, and add value to their customer base. One of the critical drivers forcing AI investment in banking is relentless competition between banks and fintech. Along with this, customers are getting more digital and tech-savvy, and this new generation of customers want to use the maximum benefit of the digital mode of services. End to end digital transformation of banks made it a necessity to harness the latest technologies, including AI, to optimize operations and bring cost efficiencies. The transformation of the banking industry with AI is still in its early stages, but the trend is visible. It is a game-changer for risk management in finance as it delivers tools and solutions to identify potential risks and fraud. Summarized below are some of the critical advantages of building a responsible AI framework for financial risk management.
AI allows handling and analyzing unstructured data, thus saving money and time of financial service companies. AI in banking risk management can lower operational, regulatory, and compliance costs and offer reliable credit scorings for credit decision-makers. AI in risk assessment can provide a fast and accurate risk assessment, using both financial and non-financial data. AI-fueled risk management solutions can also be used for model risk management, including back-testing and model validation. Banks seem to be investing in upgrading traditional fraud and cybersecurity systems with AI-based ones. Reason for this massive interest in AI is anomaly detection, an approach which identifies outliers in the dataset makes fraud detection faster and are cost-effective for banks.
Fraud detection and prevention solutions based on anomaly detection are commonly used. This requires a standard machine learning model that is trained on a continuous stream of incoming data. The model is qualified to have a baseline sense for the contents of banking operations. It can also notify a human monitor of any abnormalities so that they can be reviewed. Anomaly detection can also be used to train a machine learning model to recognize transactions processing properly. AI models for fraud detection can be used to tailor predictive and perspective analytics software. Predictive analytics offers fraud detection by analyzing data with ore trained algorithm to rate a transaction on its riskiness. Prescriptive analytics uses the predictions made from the interrelations of predictive analytics and leverages it to suggest what to do once a risk is identified. Banks can harness predictive analytics-based fraud detection to detect fraud across multiple channels in payment processing.
AI must be used responsibly by financial institutions as criminal schemes become more sophisticated. Applying AI to fraud detection and financial risk management enables firms to identify fraudulent transactions with greater accuracy. AI algorithms are capable of learning and recognizing new patterns that may have been missed by other approaches to fraud management. Given the enormous amount of information and money and the increased threat posed by cybercriminals, implementing cutting edge systems is essential as firms strive to stay ahead. The benefits that a finance organization will obtain and the money it saves using AI is unprecedented.
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