Artificial intelligence has already transformed many other industries, and finance is no exception. The industry’s leaders are accelerating their use of AI technologies to boost efficiency and reduce costs.
These efforts are paying off in areas like fraud detection and customer support, where AI can help to ensure the safety of sensitive data. However, the financial services industry must understand the full impact of AI to make the most of it.
1. Predictions
AI is helping financial institutions make educated decisions in several areas, including detecting and preventing fraud. For example, it can be used to identify patterns of suspicious activity such as credit card fraud and send alerts to both institution and customer.
This technology helps reduce costs for FIs by automating repetitive tasks like answering common questions and responding to customer inquiries. It also frees up staff to handle more complex cases. In addition, ML models can predict future revenue forecasts, stock price changes, and credit risk management. However, unforeseen circumstances not captured by data can degrade ML models and require human-in-the-loop for many use cases. Despite this, ML can be a valuable tool in making more informed and efficient decisions. It can also accelerate and automate historically manual and time-consuming activities such as market research.
2. Automation
AI is used to automate tasks, which frees up human resources and improves security measures. This can allow financial institutions to process and respond to customer requests more quickly and around-the-clock.
In risk management, AI is able to identify patterns and trends that humans may miss. It also helps lenders make better credit decisions by analyzing an applicant’s digital footprint and their spending habits.
Unfortunately, this technology can’t take away every human error. But it can help prevent some costly mistakes that could lead to legal ramifications or lost consumer trust.
3. Detection
No industry or sector has escaped the impact of artificial intelligence. From preventing credit card fraud to boosting customer satisfaction, AI-powered applications have streamlined programs and procedures, saved companies time and money, and improved the experience for customers.
AML and fraud detection, for example, requires a shift from reactive controls to proactive analysis that identifies patterns, data anomalies and suspicious relationships. AI can help achieve this by filtering out the noise generated by transactional data and reducing false positives.
The use of AI in financial services can also improve the quality of management information and reduce human biases caused by psychological or emotional factors. This is achieved by leveraging machine learning algorithms to benchmark user behavior, evaluate incoming transactions in real-time, and spot anomalies or longer-term trends that are difficult to detect using traditional methods.
4. Analysis
For companies, AI allows them to improve their internal processes by allowing quicker and easier market research, risk management, automated compliance and more. This ultimately leads to better and individualized products and services for consumers.
One example of this is how credit card fraud detection works: AI can analyze a person’s buying behavior and compare it to historical data to spot any suspicious transactions, reports Built In. This saves time and money for both the institution and the customer.
Moreover, it also helps reduce risks and conserve manpower. This is especially important in finance, where critical decisions cannot afford any errors. This is why AI is gaining traction and has become a necessity in many of the industry’s processes. Robust consumer financial protection in the era of AI will require both automation and high degrees of incisiveness to avoid consumer harm, however.
5. Decision-making
In the business world, decision-making involves assessing and weighing options to decide which course of action is most appropriate. The decision-making process usually includes four steps: perception, interpretation, judgment and implementation.
For instance, in the banking sector, artificial intelligence can expedite knowledge-based activities such as creating investment strategies. This can help reduce costs and improve performance.
According to Forbes, AI can also analyze a customer’s behavior and actions to determine their creditworthiness. This can be important since credit is a key factor in accessing favorable loan financing, finding jobs and renting apartments.
Despite these positive impacts, financial institutions must be careful not to underestimate the potential risks of using AI. Incorrect assumptions may lead to wicked consumer protection problems such as product steering, unfair credit rationing and digital redlining.
