How Are Businesses Leveraging AI in Financial Management?
“The major winners will be financial services companies that embrace technology.” – Alexander Peh, Vice President, Merchant Solutions, RBC
Artificial Intelligence has paved its way into many fast pacing industries such as food processing, real estate, and many others. It is no surprise that Artificial Intelligence (also referred to as AI) finds many applications in the world of finance and financial management. In fact, many businesses are jumping on the tech bandwagon to leverage AI in financial management. AI, through its ability to rapidly process large volumes of data, can identify patterns and trends which would otherwise have taken a large amount of time if done manually. Large corporations are making use of AI to make capital budgeting decisions and to select from various alternatives of potential investments. This helps them in maximizing their returns with minimal risks. In addition, by predicting future trends and patterns of data, AI helps in predicting future outcomes, thus considerably minimizing risk. The entire landscape of businesses carried out in the financial sector is changing its shape, thanks to AI.
Why Are Companies Leveraging AI For Their Financial Solutions?
When it comes to cutting operational expenses and increasing production, industry leaders turn to robotic process automation. Intelligent character recognition allows for the automation of a range of dull, time-consuming processes that requires thousands of hours of labor and inflated payrolls. Software with artificial intelligence validates data and provides reports based on the criteria set, evaluates documents, and pulls information from forms (applications, agreements, etc.). According to an article by Forbes, 65% of senior financial management expects positive changes from the use of AI in financial services. Ernst & Young (EY), one of the largest firms in the world providing financial services, has reduced its costs by 50%-70% by automating its operations through AI. Forbes has reckoned AI in financial services as ‘A Gateway Drug to Digital Transformation’.
The characteristic benefits of AI, that is, its 24/7 availability, reduction in human error, faster decision making, and its daily applications make it a technology that every business would want to implement. It allows financial institutions to maintain 24/7 interaction with their customers, thus enhancing customer satisfaction. Moreover, AI reduces the need for humans to carry out monotonous and repetitive work. Instead, human judgment can be applied to matters of more importance and long-term relevance. Another important benefit of AI is that it significantly reduces false positives which would have otherwise happened due to human error.
Lastly, AI helps businesses in reducing their costs by increasing the speed of transaction processing, which ultimately results in increased productivity and efficiency. An increase in operating income also helps the financial businesses in attracting potential investors. Following are some of the applications of Artificial Intelligence in Finance:
- Detection and prevention of frauds – Banks and other financial institutions essentially use the deposits made by the people to provide loans. Since these entities deal with large amounts of money, detection and prevention of fraud become their primary concern. With the use of AI and other automated software, banks address this issue of security/ AI can identify any uncharacteristic pattern of spending, for instance, if a card is used to withdraw an unusually large sum of money, it raises a red flag. In addition, when a false alarm raised by AI is corrected by humans, AI learns from this experience and makes more accurate decisions about fraud detection the next time. An example of Automated software which many banks are using presently is Core Banking System (CBS), which is a centralized software solution that is capable of automating banking transactions on a real-time basis.
- Risk assessment and minimizing – The finance sector involves a plethora of areas where predictive analysis is made. Since the very premise of AI is learning from past data, AI can help institutions in assessing relatively accurate outcomes. For instance, while granting a loan, a bank needs to assess the credit score of the customer to determine the rate of interest it needs to charge and to understand the repayment ability of the customer. For this, it analyses the data about customer spending patterns, active and repaid loans, credit scores, and so on. It assesses the credit risk of the consumers and the small business loan applicants by analyzing thousands of data points from credit bureau sources. The platform collects portfolio data and uses machine learning to identify patterns and determine which apps are good and which are harmful. This function of AI can also be recommendatory in nature, that is to say, it can find potential customers for bank loans based on their credit rating and other factors.
- Capital budgeting and other financial advisory services – Any entity, throughout its existence faces a large number of investment choices it can make. Unfortunately, the availability of funds is limited and only those projects which provide the maximum returns with minimal risk are selected. This area of capital budgeting requires both machine processing and human judgment. AI helps in determining the possible future cash inflows which the entity can expect from the investment. Based on these results, the humans decide from among the alternative investments. Thus, collaboration here becomes a key as they provide more benefits than the individual components would. Thus corporations that use AI can make more informed financial decisions that are backed up by past data.
- Trading and investments – Through the use of AI, data can be used to predict future patterns in the market and help individuals as well as corporations in holding a relatively stabilized portfolio. Through the use of past data, machines can provide personalized solutions as to when we should hold, buy or sell our stock. Of course, one should also do their own research before acting on the solutions. By predicting market trends based on the existing stock market indices and other variables, individuals can get an idea about when the market is expected to fall and when it may rise. This helps in catering to the requirements of both high risk and low risk appetite individuals. The AI helps in building a strong and stable portfolio of investments.
- Personalized banking – The banking sector has emerged with a number of innovations over the last few decades. Banks have adopted technologies and software which allow them to provide their customers with a personalized experience, catering to their every need. A very good example of this would be the chatbot service that the mobile applications of these banks offer. The chatbot, which is powered by AI, addresses the needs and complaints of the customers without any human involvement. Basic banking transactions such as deposits and withdrawals, checking account balances, and requesting a checkbook are a few of the many activities which can be executed by the customers seamlessly. Secondly, financial institutions have developed apps that, when installed by the user, track their income spending pattern and the expenses they incur and provide personalized financial advisory services based on individual needs and requirements. Moreover, the customers, by putting in their account details can enable automatic receipts and payments of routine transactions, thereby helping them in avoiding any penalties for delay.
- Corporate financing – For banks, the biggest risk in their course of business is the increase in their non-performing assets. Although various loan authorization measures are in place, AI helps in taking the authentication procedure up a notch. The predictive analytics capabilities of AI help in assessing the risk associated with a grant of loan to a particular individual. It analyses data of the customer and
- Reporting – A financial entity needs to prepare several reasons ranging from regulatory compliance to internal control. The preparation and presentation of these reports eat up a significant amount of an employee’s time. This is where AI comes in. Machine learning, artificial neural networks, and natural language processing have increased the availability and accuracy of data. This in turn reduces the aggregate time it would take an entity to prepare reports.
Now let’s talk about some of the challenges that stand in the way.
Challenges In The Way Of Using AI In Financial Management
AI’s role in the finance sector is not all gold. Issues ranging from security concerns to cost ineffectiveness to small entities have made the use of AI-based models a challenging task. Various companies shy away from implementing AI because of its large investment requirements, or the sheer unwillingness to adapt to changes.
- Transparency and building trust – An area of extreme concern in the financial sector is the security and safety of customer information. Considering that it is very likely customer information is used to develop AI models, transparency in its use and security in its storage becomes an important area of consideration for financial institutions. Customers need to be assured of the safety of their data by clearly explaining to them how their personal information is going to be used. This not only ensures that the customers are satisfied but also reduces the possibility of a legal liability arising.
- Security and legal and regulatory compliances – As mentioned previously, AI collects large volumes of data for processing, much of which is confidential and contains sensitive information. Where a financial institution enters into an agreement with a third party cloud vendor, the Service Level Agreement (SLA) needs to be carefully drafted. Adequate security options and measures coupled with quick updates need to be the basic requisites in an SLA. Proper control systems need to be in place to ensure that unauthorized access to any AI-based software takes place.
- Cost-Benefit analysis – The cost implications of installing an AI-based software need to be compared with the benefits which are expected to flow to the enterprise which is attributable to the software. Affordability can be a major barrier to small enterprises. There is no doubt that using AI will increase the efficiency in operations, ultimately increasing the revenue, but the question needs to be asked, “Are the benefits that we gain the cost that we will have to incur?”
- Human compatibility and cooperation – Various functions of AI in finance require complementary human involvement. For instance, in fraud and error detection, AI can only flag unusual transactions. The final decision, however, has to be made by a human analyst. In the battle against financial crime, the interaction between people and AI algorithms is a crucial issue, given that the final decision is often taken by human analysts and alerters. Financial institutions must make greater efforts to provide adequate navigation and investigative tools to allow the human analysts to consider those factors that the AI algorithm did not consider.
- Availability of data – AI technology requires large amounts of data and information. When exposed to increasing volumes of data, machine learning models become more efficient, thereby leading to better precision and predictability. However, it is of utmost essence that these models are fed with a constant supply of quality data. The availability of data, coupled with its security and transparency is the biggest barrier in the way of the rapid adoption of AI technology.
The use of Artificial Intelligence in the financial sector has often been related to the use of blockchain technology. The decentralization of blockchain would facilitate transaction transparency. Moreover, the fee per transaction would also be reduced owing to the lack of intermediaries when using blockchain.
Whether it’s a chatbot providing a personalized banking experience or an investment advisory service, Artificial Intelligence has tremendous scope in the financial management and financial services sector. It goes without saying that a fully operational AI model which is designed properly can do wonders to streamline business processes. Various large organizations are making long-term investments to enhance their operational efficiency and generate returns in the form of cost cutting. Although in its nascent stages, AI is seeing rapid implementation owing to its contribution to enhancing customer experience. With the speed at which financial institutions are implementing AI for financial solutions, it is safe to say that AI is the future of the finance industry.