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Despite the fact that numerous industries are increasingly embracing AI technology to drive enhanced operational efficiencies, customer experience, and financial performance, one of its earliest pioneers was the financial services industry. Since the start of securities exchanges, the ability to predict stock market movements has been an important place for institutional investors.
So far, the bulk of AI spending on capital markets has focused straight on enabling front-office trading and customer-facing business functions, while middle-and back-office operations have stayed mostly unchartered. Investment operations and accounting systems have become progressively more sophisticated in their efforts to address the ever-changing accounting standards and regulatory compliance requirements of the industry, but they are not smart to date. Rather, they are generally legacy solutions driven by hard-coded rules and processes that allow for the inflow of structured data from external sources such as custodians, counterparties, clearing and settlement systems, and exchanges of securities. Unlike front-office trading operations using AI to make more informed decisions based on deep learning and big data analysis, the middle and back office's goal is to perform regulatory compliance, accounting, and other operational tasks with maximum accuracy and efficiency.
Today's most experienced, front-office trading models were making use of advanced AI tools such as deep learning with Advanced Neural Networks (ANN) to discover new patterns and achieve insights and conclusions through data interpretation. Unfortunately, models of deep learning were not able to prove their consequences. Machine learning models need to be thoroughly trained and tested in order to produce very specific and precise results. They need to be designed to identify only suitable patterns, then suggest or trigger appropriate operational and accounting-related actions. This requires highly specialized investment operations and accounting expertise, not only in terms of mid-to-back office functions, but also across ever-growing markets, transactions, asset types, regulations, and industry operating models landscape.
The insurance sector is affected by the multifaceted aspects of AI such as machine learning, data analysis, natural language processing (NLP), robotic process automation, and decision management. AI tools have all the capabilities to bring enormous gains in efficiency and cost savings to mid-and back-office investment operations. On the other hand, to deploy these technologies effectively, it is necessary to ensure that the application provider has deep domain knowledge and expertise to genuinely optimize the capabilities and help take advantages of this innovative technology.
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