New frontiers of financial risk
Financial institutions have to take certain risks to make a profit and to minimize losses they need to implement effective risk management policies. This year, the risk environment in the financial industry has worsened significantly, with cybersecurity threats, increasing customer expectations, sweeping regulatory changes, the global pandemic, and the ensuing economic slump all capable of causing serious problems.
To withstand these challenges in the modern digital landscape, analysts and security managers need the right bank risk management software to identify potential threats, maintain regulatory compliance, achieve business growth goals, and carry out other important tasks.
According to a report by McKinsey, technology innovations don’t just bring change to customer behavior and customer expectations but open the door to new risk management approaches. With faster computers and cheaper data storage, more institutions can afford high-quality risk decision support. McKinsey named three main tech trends in this regard: big data, crowdsourcing, and machine learning.
In this post, let’s examine how machine learning and artificial intelligence are used by financial institutions to mitigate risk without loss in performance.
Self-learning systems in bank risk management software
Machine learning models provide deeper insights into data than ever before and predict financial risks more accurately due to their ability to identify complex patterns in large datasets and get smarter with every bit of new information. In the case of AI solutions, the datasets can be both structured and unstructured.
Structured data is contained in the bank’s own databases and spreadsheets. Unstructured data processing includes analysis of news articles, publicly available business reports, emails, social media posts, etc. This information is filtered and analyzed by sophisticated algorithms that glean financial intelligence from it in real-time and use it to track relevant issues and identify critical events.
AI-driven risk management solutions process immense amounts of data, liberating risk managers and analysts from time-consuming, repetitive tasks, and supporting their efforts with timely fact-based findings.
Here’s a list of risk-related tasks that are already being tackled with the help of artificial intelligence:
- financial market analysis
- interest rates research
- investment risk analysis
- regulatory compliance
- claims data analysis
- probability of default and early warning signals in credit risk management
- client screening in financial crime risk management
- behavioral analytics for asset and liability management
- balance sheet management
- securities pricing and trading
Challenges and concerns
Despite their obvious benefits, the adoption of machine learning and especially AI technologies involves certain obstacles and problems that businesses have to resolve.
First off, most risk management departments have long-established processes and tools that deliver tangible results and are familiar to the specialists that use them. The promise of higher efficiency is often not enough to push decision-makers towards rehauling the entire approach to risk management with self-learning systems.
In this same vein, the black-box nature of AI — “progress at the cost of explainability” — can become a deterrent. When it’s difficult even for developers to explain how the algorithm arrived at a particular decision, heavily regulated businesses might prefer to err on the side of caution.
Another challenge is the bias that machine learning models can show in their recommendations due to faulty assumptions and biases built into them at the development stage.
The use of AI and machine learning in bank risk management software is still in its early days due to multiple factors, from ethical concerns to the cost of implementation. However, we can confidently say that the gains from such solutions far outweigh the losses.
The financial industry is now entering the new leg of the technology race, where the lack of AI tools has the potential to severely hamper growth and leave the business vulnerable to unforeseen risk factors.
As the technology matures and more practical cases become available, we will see more firms elevating AI tools from experimental to routine use and deploying self-learning systems at scale in a wide range of applications.
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