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June 02, 2021Posted by : Career Topper Content TeamNo Comments

Artificial intelligence (“AI”) has touched all facets of operational and data processing roles in all forms of business, including finance. Role of artificial intelligence extends from automated banking, verification, faster fraud detection, investment management, compliance, financial reporting, etc. Increase in computing capabilities has increased the use of cognitive capabilities of an artificial intelligence system. The finance industry is claimed to have benefited the most with the improvement and has also been investing in developing AI-based solution for their clients such as robo-advisors, chatbots, personal assistants etc.

1. Investment Advisory Services:

Financial firms are constantly under pressure and demand to reduce the transactional fees. AI-based solutions such as robo-advisors work on Machine Learning (“ML”) technology to lower costs of customer service and operations.

Advisory Services

AI can be helpful in improving advisory services, offered to retail clients as well as institutional clients, by:

Interpreting clients' data and determining their financial goals.

Presenting a personalized financial plan for a particular client.

Improving customer service

Increasing Customer Engagement - Reach could be increased

Reducing cost of investment with less human involvement

Creating innovative financial products, using available market data such as credit scores or investment schemes.

Financial Analysis

Financial analysis of companies can be performed using machine learning concepts. Unsupervised learning techniques are used for ratio analysis or peer-to-peer analysis, whereas supervised learning techniques are used for regression analysis.

AI can be helpful in financial analysis, by:

Improving transparency by automating repetitive tasks and streamlining processes

Using Big Data analysis and techniques for processing data points

Reporting performance of investments with increased efficiency and reliability on data points

Presentation of data

Data representation is a repetitive task which also requires cognitive capabilities. It helps to accurately present and analyse the data, which is used to draw meaningful inferences. Using various data visualisation tools, this process can be made more insightful with less human intervention. Reduction in human intervention would also minimize the chances of errors and produce consistent results.

Using AI in Data Representation, users can help to draw useful inferences on the following parameters:

What information is vital to your clients?

How much detail do your clients require?

What actions can be taken with your discoveries?

How do learned insights affect current actions?

Audit simplification

Artificial intelligence has vastly improved the speed and accuracy, while conducting financial audits.

Industry-wide control points, regulatory check-points and benchmarks are used to analyse the transactions and then segregate them into high risk, medium risk, or low risk. Previously, this task would be performed manually to review the doubtful and suspicious transactions.

AI systems can help recognize transactions and data points, with an inherent possibility of error. AI can be helpful in financial audits, by:

Ensuring error-free, data-entry operations

Merging data from different and numerous sources

Identifying any breach of accounting policies and procedures

Raising alerts in case of irregular transactions, while performing data verification

Due diligence and Compliance

Regulatory reforms can have a significant impact on industries in the BFSI industry and increase the cost of on-going compliance, with additional investment in human resources. AI can help to devise automated processes for compliance and reporting, while reducing human effort and ensuring that such processes are secure and error-free.

AI can support the functions of on-going compliance and third party risk due diligence, in the following manner:

Data Collection and Data Cleansing - Screening of data using machine learning increases the efficiency for data analysis and reporting.

Intelligent Tagging and Avoiding Data Duplication - Data clustering is done to validate the datasets, which are analysed and checked for any errors or duplications. This results in significant noise reduction in the data.

Digitized Workflow and Triggers - Insights from raw data is processed and triggers are set to escalate the issue to the relevant point of contact.

Categorisation of Risk - Risk-rating algorithms and checklists identify the potential risk for the user, analysing the data and measure the severity of the risk.

Reporting - Multiple teams can co-ordinate and work on the same data set, without worrying about the risks of duplication. This can help achieve speedy due diligence and compliance.

Here's a look at the current state of AI and what lies ahead

AI is used in all facets, within the BFSI industry.

Artificial intelligence helps to analyse an increasingly large amount of data in real-time. Worldwide data will grow by 61% to 175 zettabytes by 2025. (Source: IDC)

Technology and financial service companies are currently absorbing 60% of AI talent. (Source: MMC Ventures)

63% of people prefer to message a chatbot instead of conversing with a human when communicating with a business. (Source: G2 Crowd)

80% of emerging technologies will have Artificial Intelligence technology as foundations by 2021.

(Source: Gartner)