AI for Risk Management: The Battle of Computer with Human
You might have heard—the future is automated. And according to a report by PwC, this could be true. By 2030, it’s estimated that a massive 30% of roles in the finance sector will be automated. But what does this mean exactly for fintech companies and staff, and more specifically, risk assessment AI? Surely, the human factor is not obsolete just yet?
That’s right! But before we go high-tech, let’s take a look at the factors that influence AI risk management today.
Risk Management Algorithm Today: What You Need to Know
Automation might be at the top of every company’s to-do list, but when it comes to credit risk management, as the saying goes, you can never be too careful. What are some of the factors to look out for, and how do the human and the machine compare?
Speed
When it comes to how fast risk can be analyzed, machines have the advantage. Based on intelligent patterns of AI model risk management, users can analyze risk in a matter of minutes. This is because it is able to draw from a variety of data sources and compare it much faster than a human ever could.
But is speed everything? In AI model risk management – no. Other factors do have a role to play, such as accuracy and effectiveness. However, in order to compete in today’s market loan, providers need to introduce new technology so that they don’t miss out on opportunities, while other competitors may be faster.
Accuracy
We’ve all heard of human error, and, of course, this is detrimental to business. But is technology any better? After all, are machines created by humans? In general, AI and ML-based systems for credit risk management are more efficient at dealing with data-related errors and can easily detect anomalies quickly. Technology can draw from various data sources, much more than a human can see, and alert to any potential financial risks along the way.
That’s said. Humans can detect other factors which can impact lending decisions. For example, a real-life staff member may detect nervousness which may indicate issues, or if something wasn’t quite right with the client’s loan application. But the gap is closing as machines get more intelligent than ever before. Machine learning and AI risk management empower technology to ‘learn’ based on smart algorithms, which can be trained to make smarter decisions.
Business Intelligence Risk Management
In the past year, the world has been rocked by unprecedented events. Not only has this impacted our personal lives, but on the way, we do business as well. The risk landscape is changing, and it can be a challenge to keep up with the times. Risks that were relevant even a year ago could today be obsolete, stable jobs, and incomes not so stable.
Data is everything. And it is in knowing how to analyze this data both qualitatively and quantitatively that we can predict the future fintech landscape. This is where the combination of the power of technology combined with human intelligence comes into its own. Empowered by machine-generated data, humans can make smarter loan decisions based on relevant statistics combined with an understanding of market conditions. This leads to a healthier lending landscape that, hopefully, will drive the business forward in the post-COVID era.
By integrating AI solutions with BI solutions, financial companies can gain more insights and watch real-time statistics of their performance. Business Intelligence risk management is a promising research area for credit organizations and other financial institutions today.
Costs
The hiring process can be expensive, especially when you consider how much it costs to recruit, onboard, and train your team, factor in ongoing education, and the end sum can genuinely add up. But what about technology? Surely, that’s a cheaper solution, a one-payment and done kind of deal. Not quite. Adding AI credit risk management algorithm can also be a pricey process. Before entering into any agreement to onboard new technology, it’s worth undertaking a total cost analysis (TCO) to establish the cost benefits of the technology over time. This will deliver you a more accurate sum for the value of automation for your business.
So, which one is more worth it? Both. For a business to thrive, you need a combination of human customer care mixed with the power of automation to support business processes. Technology is moving forward. Despite the cost, it’s vital to stay ahead of the curve. But be smart about it, and employ intelligent, cost-effective solutions from the get-go.
Functionality
Speed, accuracy, and cost are all great, but technology is only valuable if it does the job it is supposed to do. So, which one is better at credit risk management – the human or the machine? Humans often have a bias that can impact decision-making, even when faced with data. However, they are better at picking up on social cues, which can indicate a bigger problem down the line, as well as building consumer trust with excellent customer service.
On the other hand, automated technology and risk assessment AI can quickly verify and deal with issues using red flags by drawing from mass amounts of data. This empowers the user to verify any claims in person and move forward in a smarter way.
Human vs Machine: The Ultimate Balancing Act
As you may have guessed initially, neither human nor machine can win outright in this battle. Instead, a balancing act between technology and humanity must occur to achieve the results needed to compete in today’s market and into the future. Tech provides the sheer automation power to do this faster and more accurately. Meanwhile, humans add the natural intelligence factor and the ability to read more subtle factors that machines cannot. All that matches the concept of GiniMachine: AI in risk assessment makes data-driven suggestions, while the really important decisions are up to qualified employees.
So, the winner? It’s a combination of both.
So let’s wrap up quickly and recap the most important points. Here’s all you need to know about the automated vs. human battle:
- Financial risk is constantly evolving. What affects the market today may change entirely by tomorrow. So keeping on top of market trends, especially in risk assessment AI, is a must.
- It’s never a question of which. It’s both. One style alone no longer works for credit risk management. A combination of factors is key to making more intelligent decisions.
- Data is king in the 2020s. Big data has hit the market in a big way, and using this comprehensive resource, a loan provider can tell a whole lot more about a client than ever before, and this is empowering businesses both to make smarter decisions and also offer repayments based on risk-level.
GiniMachine in risk assessment is a powerful tool for managing credit risks. It is created to make data-based predictions for application scoring and collection scoring. Fill out the form for a quick demo.
