Since the market crashes of the late 2000s, businesses, regulators, and consumers have been more concerned with risk. Some never recovered and some are still trying to recover from everything that happened. Allowing something like that to happen again is good for no one.
Technology has helped improve some of these processes over the years. Big data has increased the amount of data companies analyze before they make decisions. Blockchain allows companies to check the validity of transactions before they are completed. Machine learning also plays a role in managing financial risk.
Machine learning is a subset of AI. We can see it in aspects of technology like self-driving cars, photo analyzers, Siri, Alex, and IBM’s Watson. It works by using massive amounts of data to solve problems. What is unique about all this data is its relationship to the program analyzing it. Machines that learn have code that adapts to the data it is analyzing. For example, a programmer does not have to change the code of the machine to adapt, it can change the code itself. All of this technological innovation allows machines to analyze huge amounts of data and reveal patterns, trends, and associations in prediction and risk management.
For financial institutions, this means they can better manage their financial risk. It used to be that people, namely data scientists, would have to clean the data, select models, cluster the data, and analyze it. That is no longer the case. Algorithms can now do those jobs and usually much more efficiently. Many experts in the field of AI say that machine learning algorithms can already outperform humans in most situations and they predict that AI will surpass human intelligence by 2019.
It is no surprise then that machine learning is becoming a vital aspect of financial institutions, especially when it comes to managing risk. Baker McKenzie recently conducted a survey where they found that 49% of leaders in finance thought that their organization would be using AI in managing financial risk within the next three years.
Machine Learning in Financial Institutions
With so much interest in machine learning and AI, there are already many use cases for how the technology is being used. The following finance tasks are just a few examples:
- Fraud detection
- Transaction data
- Behavior data
- International trade
- Portfolio management
- Mortgage risk
- Loan and insurance underwriting
For a closer look at one of the advantages of using algorithms, we can look at overfitting. Overfitting occurs when data scientists and portfolio managers look for causation or new relationships in the data that do not really exist. Repeated testing of data and tweaks to get the desired result creates overfitting. The data becomes contaminated in a way that forces it to fit into a wanted result.
Machine learning can help manage overfitting due to the fact that human influence is taken out of the equation. This will make for improved risk management as managers are not making financial decisions based on overfitted data.
Institutions that effectively use machine learning
The following institutions are effectively using machine learning to create better risk management strategies like the ones above.
PayPal uses machine learning, neural networks, and deep learning techniques to detect the risk for fraud within milliseconds of a transaction.
Some banks have instituted machine learning systems to help manage their risk. They have replaced statistical risk management with machine learning to be able to quickly and accurately scan transactions for risk and move them to a queue for further investigation. All this happens automatically and the system improves its ability to detect fraud over time, as it is exposed to more data.
Machine learning could also be used to help banks measure creditworthiness for loans. Algorithms could tap into data on spending patterns and other financial data to predict which customers would be at risk of defaulting on a loan. The bank could then create a loan agreement specific to each customer.
There are many advantages to banks: better security, better service, increased efficiency, and more. This is just the beginning of how much machine learning will improve the quality of banking services.
In collaboration with Feedzai, CO-OP released a machine learning risk management tool for credit unions. The tool is meant to find fraud regardless of the transaction volume, take in data from multiple sources, incorporate advanced analytics, work in real time, and send alerts about risks.
Challenges of Machine Learning
As the field of machine learning continues to grow, it also comes with various challenges that financial institutions have to manage in order to use it effectively.
When there is a face to a decision or there is a human involved, we sometimes trust the decision more. We can ask questions and understand how a decision was made. With an algorithm, it can be difficult to trust the decision as there is less transparency. Before using new algorithms companies need to review them and check the results. They also need to make sure that the data they input into the algorithm is ethical, accurate, and immune to manipulation. If not, the use of the data could be illegal, unethical, or just plain ineffective. There are many conditions that leaders need to meet in order to trust the results of algorithms.
2. Follow through
Even with the best algorithm, there are still many human elements to take into consideration. An algorithm can provide the most amazing results, but if there is not a person who does something with them, then they are useless.
3. False alarms
When people manage the algorithms, they have to think carefully about how they set up their risk assessment levels. If the alarm based on the data is not sensitive enough, then the risk will increase. If the alarm is too sensitive, then people will be buried in risk. They will have to sort through all the risks on a case-by-case basis, decreasing effectiveness and increasing fatigue.
A lot potential for revolutionizing financial risk management
Machine learning has a lot of potential for revolutionizing financial risk management, but there are also many challenges to overcome in order to make it as effective as it has the potential to be. Both businesses and consumers will be in a much better financial position if leaders in finance find ways to manage the complexities of algorithms and machine learning.
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