Ask any investment portfolio manager, and they will tell you that credit risk management is not a simple, 1+1 = 2 process. Conversely, it is a multi-faceted, variable process that involves everything from monitoring core business metrics and advanced analysis of data points to generating insights and decisions on risky behavior. Add to that the high stakes attached to this exercise (just ask Credit Suisse), and it is no wonder that investment management is considered one of the most stressful jobs in the financial sector. All of which makes the advent of AI in the loan portfolio management field a most welcome and game-changing one.
The evolution of AI in the investment management industry
AI has come a long way since Alan Turing first introduced the term ‘algorithms’ way back in the 1930s. Though Turing laid a lot of the groundwork for future growth, it wasn’t until the 1960s that interest in AI piqued. Interestingly, it was the finance industry that drove this newfound fascination. It was during this era that AI researchers developed the Bayesian statistics system, which later became a cornerstone of machine learning.
That said, it wasn’t until the 1980s that the statistical principal found practical applications within the credit risk sector. One of the first instances of this was the Personal Financial Planning System (PFPS) introduced by Chase Lincoln First Bank in 1987. This AI-augmented system helped with, among other things, investment auditing.
Throughout the 90s and early 2000s, most of the AI development in the fintech industry focused on fraud detection and prevention. Then, the 2008 financial crisis hit, and with it came a greater focus on risk management and mitigation. Consequently, AI-focused research and development in the investment industry flourished. At present, around 50% of lenders and investment institutions use AI to meet their risk assessment needs. This number is only set to grow in the coming years.
6 Things AI brings to the investment management table
1. AI speeds up the portfolio-monitoring process
The traditional portfolio-monitoring process is a lengthy and laborious one. First, managers must monitor a veritable mountain of data on their borrowers. This information includes their tax disclosures, external delinquency amounts, loan utilization aggregates, current solvency reports, and cash flow, amongst others.
Then, they must take the time to review each data point, verify its authenticity, and study its impact. In addition, managers must keep an eye on stock market fluctuations and their borrower’s social index as well. Only after all the monitoring, sifting through, analyzing, and stress testing can a manager get a holistic view of the ongoing creditworthiness of their portfolio.
An investment manager at work
Given that, it’s safe to say that manually evaluating every account within a portfolio would take weeks, if not months. AI can cut this time down massively. For example, AI-augmented solutions such as TRaiCE can monitor thousands of accounts in just a few minutes.
2. AI improves efficiency
Speed without efficiency is like a lock without a key – not very useful. Fortunately, AI improves not only speed but also the efficiency and reliability with which risk management is done.
In the past, the investment management industry relied heavily on linear-regression-based methods of calculating risk. While these models are easier to interpret and understand, they are inherently flawed because of their one-dimensional configuration. At its core, risk management is a non-linear process with complex connections and patterns that traditional models are ill-equipped to identify.
Advanced machine-learning models, on the other hand, can effortlessly connect the dots between the many variables, giving you a more authentic default risk measure. And don’t take our word for it. There’s a ton of research out there that shows ML-backed models consistently outperforming the more traditional ones. What’s more, these AI-backed systems can also detect risk-affecting variables and data clusters hidden to the human eye, thereby reducing the number of false positives and negatives.
3. AI facilitates compliance
Since the 2008 financial crisis, the number of regulations imposed on financial institutions has increased exponentially. In fact, according to Thomson Reuter’s ‘Cost of Compliance 2018’ report, governing institutes issue regulatory updates every 7 minutes! Furthermore, to show they mean business, regulatory bodies have doled out fines worth over $240 billion in the last decade alone. A failure to comply is therefore not an option for the investment sector. Unsurprisingly, the compliance workforce has doubled from what it was pre-2008. The industry has also moved from a dormant advisory role to an active administrative one within the risk management sector.
Given the sheer volume of regulatory data in need of monitoring, compliance departments can benefit greatly from AI. Machine learning models can analyze available data to pinpoint systemic issues and patterns in employee or customer activity that require remedial action. Importantly, portfolio managers can use the metrics and KPIs obtained from this data analysis for compliance submissions, allowing them to kill two birds with one stone.
4. AI helps mitigate losses
There’s no doubt that the most important advantage of using an AI-augmented system for investment management is its predictive power. Traditional early-warning systems rely greatly on human expertise. Risk experts, in turn, base their predictions heavily on a defined set of indicators such as historical experiences with the borrower and so on.
Investment risk managing software is not limited to these parameters. The resulting expanded dataset allows models to identify default-prone entities with better accuracy and alacrity. It can also ‘see into the future’ farther than human faculties can. That it raises these red flags even before the default happens is particularly advantageous. It means that managers can keep an eye on their risky customers at all times. More importantly, defaulters rarely catch them unawares and they can pull the plug on their investments before a loss is incurred.
5. AI grows with you
Investment management is a dynamic field where parameters are rarely static. A prime example of that is the Coronavirus pandemic-induced financial upheaval in 2020. Hence, having an application that can function efficiently, even when the goalposts are constantly shifting, is of paramount importance. AI-backed software can identify patterns, make inferences from data, and become more intuitive over time. In other words, it can self-learn. It is in a continuous cycle of growth, augmenting itself with each infusion of data.
In addition, ML-backed models remember your actions as an end-user. In that sense, they are the elephants of the software world! What’s great is that these applications change their parameters in accordance with your actions and other benchmarks you have set. The end result is an optimized user experience. More importantly, you have a management system that grows with you to accommodate your escalating business needs.
6. AI drives down costs
The last, but certainly not the least, advantage AI brings to the table is that it drives down costs. While financial institutions can expect an initial investment with AI, the ROI is usually worth it. And this is in terms of both time and money. Automating the risk monitoring process frees up your staff to do other important work. This, in turn, improves their productivity and consequently your profit margins. According to a study by McKinsey, AI can save the global finance industry a whopping $700 billion. It can also free up over 50% of an analyst’s time.
It is important to note that AI is not the be-all and end-all of investment management. While it augments the process, it cannot perform the decision-making tasks that require a higher level of contextual understanding and intuition that industry experts possess. That said, it certainly makes things easier, faster, and more efficient.
Investment monitoring software such as TRaiCE can give you a distinct market advantage with its ability to streamline the portfolio management process. It can analyze huge datasets in a fraction of the time it would take a team of analysts to do. Moreover, the self-learning models that TRaiCE uses continually learn from augmented data and produce risk ratings in an easy-to-understand format. You can then use these values to decide whether a customer is treading into high-risk territory or not.
If you haven’t already jumped on the AI-driven investment-management bandwagon, you should. As risk is an area that keeps evolving, so should your management of it.