Banking institutions have to deal with numerous operational difficulties on a daily basis, with a major focus being on dealing with the challenges in the risk management process. This is due to the fact that banks have to face millions of losses every year owing to poor assessment of fraudulent activities.
However, with technological advancements in the form of artificial intelligence, financial institutions have been able to maximize their strength in the form of enhanced security and understanding against the various types of financial risks in the global market.
Key Stats/Facts
- As per the McKinsey Global Institute, artificial intelligence and machine learning can possibly generate around $250 billion in the banking sector in the coming times.
- According to the Narrative Science and the National Business Research Institute survey, 32% of professionals working in financial services admitted to using AI technologies like recommendation engines, predictive analytics, voice recognition, and response.
- Stats by Statista reveal that cards and payments are two of the biggest banking areas that make use of AI.
- Taking an example of JPMorgan Chase, it has invested heavily in technology and also designed a Contract Intelligence (COiN) platform recently for analyzing legal documents, extracting essential clauses and data points.
- Similarly, Bank of America invested in virtual assistant AI technology for “predictive analytics/cognitive messaging” to give financial assistance to the firm’s more than 45 million customers.
5 Major Risk Areas in Banks
1. Credit Risk
The main banking activity is nothing but lending. Credit risk is caused to the banks when a customer is experiencing financial issues and is unable to return the money to the bank which he has borrowed.
2. Liquidity Risk
Having less or no liquid assets for compensating the depositors’ withdrawals or cash needs and loan demands is defined as liquidity risk.
3. Market Risk
Banks are highly engaged in different activities in the market. This risk is related to the assets of the bank where their total value is changed by various factors that influence the market.
4. Interest Rate Risk
Today, interest rates change as per demand and supply conditions. Keeping these in mind, the rate decided by the banks also affects its income and expenses.
5. Earning Risk
Due to the changes in law and regulations, along with the competition, the net income of the bank gets affected severely, opening doors of earning risk.
This otherwise seems difficult if you want to learn all these skills and use them in your working environment. But it has become very easy to learn all this; you can learn them through an online banking certification course.
Top Ways AI is Refining Bank Risk Management
- Detecting Fraudulent Activities
Fraudulent activities have simply increased over the period of time, with fraudsters adopting new techniques now and then to exploit bank loopholes. This makes it difficult for the banks to deal with issues like money laundering, identity theft, and more. However, using AI’s great data analysis power, unusual patterns across different channels can be spotted easily and alerts can be sent instantly.
- Assessing Credit Behaviours
Of course, there is risk involved every time you give money to someone, and in the case of banks, they live with this risk every day. There are circumstances when someone’s regular flow of income is affected and he is unlikely to pay the debt in the future. Using AI, such patterns in the credit histories of the customers can be discovered and assessed timely and more efficiently than humans, revealing additional vulnerabilities and reducing risk eventually.
- In-depth Market Analysis
AI is equipped perfectly for reducing the market trading risk. Vast data volume processing that takes days for humans can be done using technology within seconds. The insights provide traders with optimal price points, enabling greater forecast accuracy and reducing risk.
- Enhancing Security
By augmenting huge data processing technology, Artificial Intelligence can be used to develop better alert and detection tactics. For instance, Microsoft’s Azure Sentinel is a perfect example of machine learning-AI-focused and cloud-based Security Information and Event Management (SIEM).
Conclusion
Technology is surely making its presence felt in every sector, with artificial intelligence and machine learning leading from the front. The banking sector has realized the importance of using AI in its processes due to the unmatched benefits it offers. This trend is surely going to increase with the passing of time. Yes, there might be advancements in fraudulent activities, but advancements in AI will always be on the higher side to provide security to the banks against several risks.