Risk assessment and fraud detection with AI: Identifying risky borrowers/applications, detecting fraud, managing cybersecurity threats.
AI-powered investment and wealth management solutions: Analyzing market data to identify trends, making portfolio recommendations to clients.
Loan and credit analysis: Adopting an AI-based loan and credit system, analyzing behavior and patterns of customers with limited credit history.
Process automation: Increase operational efficiency and accuracy, automate time-consuming, repetitive tasks.
Regulatory compliance: Using AI and machine learning to read new compliance requirements for financial institutions, improve decision-making process.
These applications highlight the versatility and potential of the use of AI in banking, driving the industry toward a more intelligent and customer-centric future.
AI in banking: Safeguard data, privacy, security, and trust
Nearly nine in 10 analytics and IT leaders are making data afghanistan phone number list management a high priority in their AI strategy. Banks are laser-focused on keeping their data secure: It’s fundamental to building trust with customers. Yet nearly half of executives say they believe AI introduces security risksOpens in a new window, while 59% of consumers say they don’t believe AI is secureOpens in a new window. Banking regulators are concerned as well, especially when it comes to generative AI, which relies on large language modelsOpens in a new window (LLM) to generate responses.
“Getting your data in order is fundamental,” says Amir Madjlessi, Managing Director and Banking Industry Advisor at Salesforce. “You need to evaluate the quality and quantity of your data and, if necessary, upgrade data collection and management processes. Without those steps, your AI won’t be able to extract relevant and accurate insights from your systems.”
Once you’ve prepped your data, deploying AI in banking requires further unique data management, with varying access rights for different functions. For example, to follow fair lending practices, banks must hide demographic information like religion or country of origin from lending officers. But that same information must be available to regulators as evidence of fair lending.
Data management is even more complex when it comes to generative AI, which relies on LLMs to learn how to properly respond to prompts. Leveraging solutions that have built-in data integrity like ethical guardrails can help banks address data challenges and meet compliance rules. Salesforce, for example, has a zero data retention policy for LLMs — we don’t share client data with external LLMs.