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Artificial Intelligence in Banking: Examples of real applications

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04 June 2021

The rise of Artificial Intelligence has set the path for a new era in the finance world where guesswork has no place anymore and database decision-making improvements allow cost and risk reduction, along with function optimization. Here are some of the most important applications of AI today.

Risk Management

AI facilitates processing a large amount of data and finding hidden patterns which can reduce operational, regulatory, and compliance costs. Within risk management, AI can be applied in areas such as credit risk or capital optimization, allowing financial entities to assess the degree of risk more accurately. This in turn, facilitates the prompt delivery of reliable credit scores which improve the decision-making process. In the case of capital optimization, AI and machine learning can significantly improve the accuracy and speed when calculating Risk-Weighted Assets maximizing capital returns.

The COVID pandemic is just the latest accelerator for financial entities to explore machine learning for liquidity management which facilitates the analysis of complex non-linear elements. When applied to asset or liability management, AI mitigates financial risks from a disparity of assets and liabilities. Also, unsupervised machine learning is being used by institutions to perform back testing analysis of their risk models. Furthermore, machine learning algorithms performing a stress test on financial models could predict the term structure of corporate default probabilities.

Compliance

The newly RegTech companies rely heavily on AI to assure normative compliance in different areas and avoid sanctions that might ruin a financial institution’s reputation. In fact, AI solutions are tools of great value used to monitor transactions quickly which could help detect possible suspicious movements, early on. To this end, banks are allocating large amounts of resources for the prevention of money laundering and financing of terrorism campaigns. AI surpasses traditional fraud detection systems that can’t keep up with emerging fraud strategies. Leading industry companies are currently deploying machine learning tools to find spending patterns and reduce their fraud rate significantly.

Similarly, in an effort to increase market integrity by penalizing inside trading, unlawful information disclosure, and market abuse, the European Union created the Market Abuse Regulation (MAR) and the Markets In Financial Instruments Directive II (MiFID II), a legislative framework to regulate financial markets and improve investor protection. The use of AI is a major component in both contexts.

New technologies in Operations

The application of AI in middle office and back office operations has fastened and automatized data processing, a big improvement in comparison to costly and slow traditional methods. Technologies such as Robotic Process Automation (RPA) minimizes errors and lowers back office costs. However, this technology works only on pre-established scenarios, having then the lack of flexibility as a disadvantage. Applying AI to RPA in the form of Intelligence Process Automation (IPA) enables robots to adapt to new situations while thanks to combining Natural Language Processing (NLP) and ML, robots can subtract important information from complex text structures and improve performance by learning. Another application in operations is in settlement of securities where ML is used to resolve anomalies, as it evaluates and manages the circumstances in a fraction of the time required by a human.

On the other hand, NLP and machine learning can save time and money when reading and analyzing contracts can take thousands of hours. In the US, major financial institutions use these new technologies to create tools that read and analyze contracts in a few seconds and with very few errors. AI can also significantly help with the arduous and complex process of settling operations such as bonds, stocks, etc. and machine learning can analyze and resolve anomalies in little time compared to the back office or middle office.

Commercial Banking

Chatbots and virtual assistants are already integrated in the commercial banking area, which oversees the relationship between the financial institution and the client. By automatizing simple tasks, they optimize the client’s problem resolution while creating a more complete customer profile. Clients can interact with chatbots or virtual assistants throughout multiple channels, such as text or audio, improving customer satisfaction, reducing costs, and in some cases, generating sales.

Financial institutions want to know their customer journey at every step of their business interaction. From when a client opens an account to when they start using the entity’s app or contact the call center, it’s vital for financial institutions to understand the journey in order to analyze consumer behavior, understand their preferences and define the next-best action or offer hyper-personalized experiences.

New technologies such as machine learning, permit them to identify and classify their clients, analyze their behavior, allowing companies to offer tailor-made solutions to their clients. The Second Payment Services Directive (PSD2) intends to increase competition, innovation, and transparency in the European payments market. By pushing payment service providers to strengthen customer authentication systems, these are implementing technologies such as biometric authentication, which may be conflicted with privacy concerns.

Assets Management

Asset management has greatly improved thanks to new AI technological applications, facilitating complex and intensive tasks, which were impossible with traditional methods. However, only 10% of asset management companies are incorporating this new technology in their activities, in the last year. Banks benefit from using AI and ML in the analysis of their data, this translates to better market predictions and increased profit margins. Machine learning can also be used to analyze market price trends, identify opportunities and allow entities to capture non-linearities, make estimates and see correlations between portfolio assets, among other things.

The use of AI, NPL and ML technologies is proving helpful in predicting the market sentiment, which in turn, helps make better investment decisions. In fact, algorithms known as execution algorithms are not only able to analyze the transaction history and price history of a given market or counterparty, but they can also make recommendations to ensure that the operation is carried out in the best way possible.

Corporate banking

Many entities have embraced new technologies in order to optimize corporate banking processes and activities such as expediting the analysis of big amounts of data to improve decision-making with the help of ML. Data-driven AI allowed the development of Deep Hedging, which replaces traditional risk coverage models used for defining the portfolio product prices. 

Dynamic pricing is a complex procedure which requires that several financial components such as the term structure of interest rates and credit spread, be evaluated and where applying AI has made a difference. AI can make Dynamic Pricing better and easier by providing continuous prediction, analyzing data in real time to segment customers and establishing the settings of the price optimization models. This allows strategies to be adjusted to the conditions of the market.

New technologies such as AI or machine learning have infinite applicability in the world of finance. The sheer volume of data recommends the usage of these technologies because of the speed and efficiency of how they process them. The fact that there are many examples of how financial institutions from around the world have decided to implement them, is a positive step towards the digitalization of the finance sector where traditional models and ways of working are left behind.

Get more information and donwload our whitepaper Artificial Intelligence in the Financial Sector, Open Innovation and Ethical Commitment.

 

 

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