Why AI financial risk analysis is becoming the silent guardian of our modern economy
That mini heart attack when your card gets declined overseas
You’ve been there before: finally on that dream trip of yours to either Tokyo or Bangkok. You try to pay for a fabulous meal but clink! The card does not go through! You’re a little embarrassed and annoyed. Shortly after you receive a notification or a call from your bank to see whether or not that was actually you. Though it is frustrating at the time, this is an indication that something very smart is happening behind the scenes. It isn’t like it used to be; it has become much more complex than an “if-then” relationship. This is a textbook case of AI financial risk analysis.
The bank’s systems have knowledge of your usual spending patterns. Such as getting your morning coffee while in KL or getting petrol in PJ. If you suddenly make a very large purchase from an outside country, these systems will automatically flag that transaction. This all works together in a much larger ecosystem known as AI financial risk analysis. In the past, banks relied on rigid filters that were unable to efficiently the clever criminals who found ways around the bank’s anti-fraud systems. Due to these types of systems being able to learn by experience, life for fraudsters has become more difficult and for all of us. Life is now much easier.
The “Crystal Ball” of banking: AI financial risk analysis
Although many believe banking is simply the act of storing monetary assets within a corporeal vault, those who manage banks inherently understand that banking is essentially an interactive “What If?” game. For example, what if real estate values decline? What if interest rates rise again? These “What Ifs” are a large part of the Value of Predictive Analytics for Financial Services phenomenon. Rather than looking only at what has happened over the last thirty days, banks employ Machine Learning Models for Financial Services . It help to create multiple simulated scenarios. Like an airline pilot uses a flight simulator prior to ever piloting an actual airline.
By utilizing the process referred to as AI financial risk analysis, banks can quickly ascertain their level of preparedness with respect to potential future emerging financial “storms.” When stress testing indicates a particular institution has an excessive amount of risk exposure within any one area or geographic region. That institution is capable of modifying its business strategy, prior to actually experiencing adverse consequences. Ultimately, utilizing such technologies enables banks to take a proactive approach to risk mitigation. Rather than waiting until an adverse loan default has appeared on the news headlines before taking appropriate risk mitigation actions.
Speed is the ultimate shield
The biggest advantage of AI financial risk analysis is its ability to perform Real-time risk monitoring. By analyzing millions of data points in milliseconds, AI catches threats that human analysts would only spot days—or weeks—later.
Why it’s easier (and safer) to get a loan today
Applying for a loan used to involve presenting a significant volume of documentation and waiting 14 days before being notified of approval or otherwise. Much of the lag experienced when seeking approval for a loan occurred as bank representatives had to verify your income, spending patterns and history manually. Today, many financial institutions utilize machine-learning-based credit risk assessment tools in efforts . To expedite the loan process by determining a customer’s “risk scoring” almost instantaneously. Although speed is a key benefit of utilizing machine learning, one of its greatest advantages is that, unlike traditional methodology. Machine learning enables the ability of lenders to assess loan applicants based on alternative data as opposed to solely relying on credit (traditional) scoring/rating methodologies (i.e., payment history of utilities and cellular phones).
By providing lenders with the ability to determine creditworthiness of applicants using machine learning-based assessments, lenders are comfortable in lending money to consumers/merchants that would otherwise have been disqualified. More importantly, this method makes the banking and lending industries much more accessible to young consumers and small business operators by minimizing the unnecessary required documentation typically required by lenders.
Catching the “Ghost” in the machine: Fraud detection & prevention
The sophistication of online fraud has increased dramatically. All of us have received those SMS texts or “package delivery” alerts that appear 100% legitimate. Not only are then scams moving quickly, but also there have also been advances in the use of Artificial Intelligence in the detection and prevention of fraudulent activities. In addition to the financial institutions verifying fraud through your reports, the also utilise Operational Risk Detection to detect unusual behaviour within their networks. As an example, if a large number of accounts begin sending small amounts of money to an unknown off-shore account concurrently, the AI will tag this as potentially being part of a Money Mule scheme.
This is the area where Regulatory Compliance and Artificial Intelligence intersect. Financial Institutions are obligated by law to combat money laundering and the funding of terrorism. This is obviously impossible due to the high quantity (billions) of transactions taking place daily. AI can operate in 24/7 in a manner similar to a security guard, and provides the capability of never tiring, taking breaks or forgetting the individuals (or accounts) it has previously viewed.
Real-time risk monitoring: Keeping the markets steady
The things that can happen during market hours in the world of stocks or cryptocurrency seem to happen at a breakneck speed. When one little tweet or piece of news hits, all of sudden prices go up or down in a heartbeat. For large institutional investors, handling that is a major challenge. Portfolio risk management tools utilized by institutional investors help to ensure they do not “put all their eggs in one basket.” If the AI detects one sector becoming increasingly volatile, an AI Portfolio management algorithm will likely recommend rebalancing the investment. It help to avoid significant market crashes, which are damaging to the smaller “retail” investor.
Algorithmic trading has added a level of risk to the process. But AI mitigates these algorithmic trading risks by ensuring machines don’t begin selling everything as the result of a computer failure or issue on the market floor. Finding equilibrium is what it is all about. While technology increases the number of options we have for trading, technology is also helping to keep the “game” fair and stable for all participants by creating more stable trading environments.
