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Machine learning and artificial intelligence are not the only technologies used by modern banking, however, they are the most promising tools for improving operational effectiveness and leveraging business risks. SPD Group has the expertise in implementing AI solutions for banks and shares some insights that will be quite useful for owners of financial companies owners who are on the way to AI and ML adoption.
What is ML in Banking?
Machine learning in banking refers to the whole set of innovative technologies that may be used in different banking sectors to deal with core business tasks. What’s more, machine learning and artificial intelligence technologies are quite flexible, versatile, and already affordable, and thus, every financial institution can use the best and most effective features to meet their needs.
How is Machine Learning Used in Banking?
The scope of machine learning usage in banking can be divided into four areas:
Data analysis is the main task machine learning models deal with. What’s more, the usage of machine learning without data makes no sense since a provided data set works as its pool of knowledge and the foundation for making reasonable decisions, conclusions, and predictions.
Machine learning technologies can be used to analyze huge amounts of data to effectively assess the risks each financial institution faces.
According to the Machine Learning in Banking Risk Management research, “Banks are faced with various risks—interest rate risk, market risk, credit risk, off-balance-sheet risk, technology, and operational risk, foreign exchange risk, country or sovereign risk, liquidity risk, liquidity risk, and insolvency risk. Effective management of these risks is key to a bank’s performance.”
What’s more, most of the above risks may be predicted and their probability may be reasonably assessed with the help of machine learning in banking.
Predictions and Conclusions
Having a data array that consists of historical and real-time data, artificial intelligence in banking is effective at making accurate predictions and conclusions. For example, it becomes possible to more accurately predict investment attractiveness based on an analysis of previous investment strategies, or a financial service that a client may need based on an analysis of their behavior.
Artificial intelligence in banking may work as a self-sufficient tool, for example, when the AI model takes the form of a chatbot. This is an already adopted practice in the field of finance that is aimed at improving customer service and reducing the workload on the human support team.
Is Machine Learning Useful in Finance?
Artificial intelligence and machine learning in banking are quite useful technologies especially considering the fact that an ML-powered model may be developed according to specific business needs.
The following statistics serve as clear evidence of machine learning’s potential for the financial industry:
- 82% of artificial intelligence in banking use cases count for risk management, followed by performance analysis and reporting (74%).
- The deep learning applications market is predicted to reach $80 million by 2025 in the US only.
- 97% of mobile users use voice chatbots on their mobile devices.
- 84% of companies globally believe investing in AI is promising as it may make them more competitive.
AI/ML-Powered Systems vs Old Approaches in Banking
The effectiveness of artificial intelligence and machine learning in banking becomes especially clear when compared to outdated systems that were unable to perform most of the tasks ML successfully does. Compared with old approaches to data analysis, risk management, customer service, and credit card fraud detection using machine learning, the latter is fast, efficient, and secure.
|Machine learning systems are able to analyze data in real-time making data-driven and reasonable conclusions, which make them extremely fast.||ML models are quite efficient and accurate since they are powered by a huge data array and tailored to specific business needs.||Artificial intelligence in banking is quite secure, and that’s why this technology is used for fraud detection and prevention.|
How is Machine Learning Being Used in Investing?
As discussed above, machine learning is great for the investment banking sector. The technology is quite accurate when it comes to risk identification, evaluation, and assessment, and that’s why it may be used in investment strategy development.
Surely, it still needs human assistance, however, the ML-powered suggestions are always data-driven and clear from emotions. Thus, being assisted by the ML model, the bank has a better chance of developing a really profitable and low-risk investment strategy.
Bank Fraud Detection with Machine Learning
Bank fraud detection with machine learning is one more promising opportunity for financial institutions since online fraud being provoked by the recent pandemic is on the rise right now. Below are three core fraudulent activities ML and AI in banking may help with:
- Credit card fraud detection. Credit card fraud is one of the most widespread types of financial fraud, and dealing with this issue should be one of the core priorities. Credit card fraud detection using machine learning is a promising way to deal with this type of fraud since the ML model analyzes customers’ behaviors in real-time and draws a clear line between legal and potentially fraudulent activity.
- Identity theft. Credit card fraud is followed by identity theft, and in this case, ML may be helpful as well.
- Combating money laundering and terrorist financing. The ability of a machine learning system to analyze huge data arrays makes it possible to find out whether a particular customer is involved in crimes, like money laundering and terrorism financing, and block the transaction if it’s suspicious.
Artificial Intelligence in Banking for Profitable Loans Issuing
Artificial intelligence has been successfully used to assess the potential profitability or risk of loans. In this case, the algorithm also analyzes the entire set of data about the client, including their credit history, transactions, tax payments, salary level, and possible involvement in illegal activities. The analysis determines whether the loan will be repaid on time. Thus, the bank can protect itself from risky transactions and increase the likelihood of receiving its interest on the loan issued.
Machine Learning in Banking for Marketing
Machine learning is also finding its way into marketing for financial institutions. Analysis of clients’ behaviors can suggest which financial services they may be interested in at the moment, and what personalized conditions may motivate them to accept the terms of transactions.
Artificial intelligence and machine learning can be used in almost all banking sectors, improving the performance of each of them. What’s more, in 2021, these technologies are becoming even more affordable and ubiquitous, so consider including their adoption in your financial company’s development strategy.