{"id":245942,"date":"2021-11-17T09:04:37","date_gmt":"2021-11-17T09:04:37","guid":{"rendered":"https:\/\/imarticus.org\/?p=245942"},"modified":"2022-10-12T07:24:24","modified_gmt":"2022-10-12T07:24:24","slug":"with-rampant-use-of-artificial-intelligence-and-machine-learning-how-are-financial-institutions-dealiing-with-problems-related-to-data-bias-and-transparency","status":"publish","type":"post","link":"https:\/\/imarticus.org\/blog\/with-rampant-use-of-artificial-intelligence-and-machine-learning-how-are-financial-institutions-dealiing-with-problems-related-to-data-bias-and-transparency\/","title":{"rendered":"With rampant use of artificial intelligence and machine learning, how are financial institutions dealiing with problems related to data bias and transparency?"},"content":{"rendered":"

The public and private sectors are increasingly turning to machine learning (ML) algorithms and artificial intelligence (AI) systems to automate every decision-making process, and financial institutions are no exception. <\/span><\/p>\n

In addition to widespread use in the capital markets, <\/span>artificial intelligence and machine learning<\/span> are used in financial services to make insurance decisions, monitor user behavior, recruitments, fraud detection, credit referencing, and underwriting loans. <\/span><\/p>\n

However, while AI and ML have brought innumerable benefits to financial institutions, they also have their share of woes in the form of data biases and transparency issues. <\/span>The question is, how are financial institutions dealing with these problems?<\/span><\/p>\n

Bias and Transparency in the AI Context<\/span><\/h2>\n

AI systems are powered by algorithms that \u201ctrain\u201d by reviewing massive datasets to ultimately identify patterns and make decisions based on the observations. Hence, these systems are no better than the fed data, resulting in unconscious data biases. <\/span><\/p>\n

On the contrary, transparency in the context of AI refers to the ability to explain AI-based decisions. Given the increasingly complex findings and algorithms, ensuring transparency to different stakeholders is vital in the financial sector, both from compliance and business value perspectives.<\/span><\/p>\n

Biases can occur in many ways. For example, bias due to incomplete data occurs when the AI system has been trained on data that is not representative of the population. <\/span><\/p>\n

Likewise, the dataset could be biased towards previous decision-making processes, the programmer may introduce their own bias into codes, or business policies pertaining to AI decisions could be biased themselves. The bias<\/span>\u00a0of any form eventually leads to unfairness and inequities in financial services.<\/span><\/p>\n

Dealing With AI Bias and Transparency<\/span><\/h2>\n

Although the use of AI and ML give rise to data bias and transparency issues, they have become indispensable for the functioning of financial services. So, the only course of action left to financial institutions is to adopt ways to get around the problems. Some of them are listed below:<\/span><\/p>\n