Artificial intelligence (AI) and machine learning (ML) technologies are creating waves in the financial services landscape. The banking industry, which relies heavily on data usage, is increasingly beginning to adopt these technologies and has begun leveraging its powerful capabilities. Discover the power of artificial intelligence in credit assessment.
From chatbots to fraud detection, the banking sector is using AI and ML not only to automate processes and streamline operations for both front and back offices but also to enhance the overall customer experience. AI and ML tools, with their advanced prediction techniques and capabilities to use large amounts of data, are increasingly being used in risk management to make quicker and more efficient credit, investment, and business-related decisions. The transformative power of this technology concerning applications, key benefits, and use cases is outlined below:
Applications And Key Benefits
Artificial intelligence is increasingly recognized across industries for its ability to significantly change the day-to-day activities of a business. This has been made possible by the ability of technologies to handle and analyze large amounts of unstructured data at a fast pace with significantly lower levels of human intervention. The technology has enabled banks and financial institutions to reduce operating, regulatory, and compliance costs, while also providing banks with the ability to make accurate credit decisions.
AI / ML solutions are therefore capable of generating large amounts of timely, accurate data, allowing financial institutions to build capacity around customer intelligence, enable successful implementation of strategies, and minimize potential losses.
AI / ML-driven risk management solutions can also be used for Model Risk Management (back-testing and model verification) and stress testing, as required by global prudential regulators, and may have the following major benefits:
Superior Forecasting Accuracy:
Traditional regression models do not adequately capture the non-linear relationship between the macroeconomy and the financial situation of a company, especially in the event of a stressed scenario. Machine learning provides better forecasting accuracy due to the model’s ability to capture nonlinear effects between scenario variables and risk factors.
Optimized Variable Selection Process
Feature/variable extraction processes take considerable time for risk models used for internal decision-making purposes. ML algorithms augmented with the big data analytics platform can process large amounts of data and extract many variables. A rich feature set with extensive coverage of risk factors can lead to robust, data-driven risk models for stress testing.
Richer Data Segmentation
Proper descriptions and segmentation are important for dealing with a changing portfolio structure. ML algorithms enable better segmentation and consider many features of segmentation data. By using unsupervised ML algorithms, a combination of both distance and density-based approaches to clustering becomes a possibility, resulting in high modeling accuracy and explanatory power.
Credit Risk Modeling
Because machine learning (ML) models are challenging to comprehend and difficult to verify for regulatory purposes, banks have historically used classical credit risk models to forecast categorical, continuous, or binary outcome variables (default/non-default). However, they may still be applied to enhance the variable selection procedure and optimize parameters in current regulatory models.
AI-based decision tree techniques can result in easily detectable and logical decision rules despite having non-linear characters. Untrained learning techniques can be used to locate data for traditional credit risk modeling, while classification methods such as support vector machines can predict key credit risk characteristics such as PD or LGD for loans. Financial services companies are also increasingly employing external consultants who use deep learning methods to develop their revenue forecasting models under stress scenarios.
Banks have been using machine learning methodologies for credit card portfolios for years, with credit card transactions banks get a rich source of data on which unsupervised learning algorithms can be processed and trained. These algorithms have historically been highly accurate in predicting credit card fraud due to the availability of models to develop, train, and validate large amounts of data. Credit card payment systems consist of workflow engines that monitor card transactions to assess the likelihood of fraud. The rich transaction history available for credit card portfolios provides banks with the ability to distinguish between specific features present in fraudulent and non-fraudulent transactions.
Techniques such as natural language processing and text mining are increasingly being used to monitor merchant activity for rogue trading, insider trading, and market manipulation.
By analyzing call times combined with email traffic and calendar-related data, check-in/check-out times, and trading portfolio data, systems can estimate the likelihood of Trader misconduct, saving millions in reputational and market risk for financial institutions.
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