As the name depicts, Artificial Intelligence (AI) means the intelligence demonstrated by machines, hence it is also known as machine intelligence. AI driven devices are trained using volumes of historical data so that they can use their cognitive features to automatically perform the required tasks. In some cases, these devices surpass humans, when it comes to problem solving. The most common functions performed by these intelligent devices are visual perception, problem-solving, decision making, and speech recognition.
You may have heard the term machine learning and might be wondering how it is related to artificial intelligence? Machine learning is a subset of AI, that allows the machines to learn automatically and upgrade themselves from experience. Machine learning emphasizes on building computer applications that can access large datasets and utilize them to learn.
Though the impact of Artificial Intelligence can be seen ubiquitous, the financial industry is deemed as one of the earliest embracers of this marvelous technology. If we are asked to name one sector that profited from AI the most, our answer will be the finance sector. It is expected that in the future, AI can replace humans because these systems are performing better than us. Therefore, many big financial institutions are investing heavily in AI, owing to its undeniable benefits.
Artificial Intelligence in finance has opened new avenues by optimizing the business operations and eliminating the security woes, thus increasing the customer’s trust in financial institutions. AI finance applications give customers a more secure and easier way to save, spend, and manage their money. In this article, we will specifically talk about how the financial industry is leveraging AI to address various challenges.
Training models using historical data is the foundation of AI technology. Hence, the success of AI finance systems was indispensable, because in this industry, bookkeeping and records are very common. Proper risk assessment is emphasized in the financial sector because the risks can be catastrophic for any financial institution. Timely and precise predictions about potential risks are critical for the safety of many businesses associated with the financial institutions.
Financial institutions are employing machine learning models for predicting trends, determining risks, and making informed decisions. Human employees may ignore a potential risk, and consequently make a decision that can prove costly for the business. While a single wrong decision made by a human can cost a financial institution millions of dollars, the likelihood of mistake by a machine learning model is very low. This is because machine learning models continuously learn from a huge dataset to make accurate decisions. Besides aiding in decision making, these models also save time by automating the entire process. It becomes extremely challenging for human employees to consider the unstructured data while assessing and managing the risks. The ability of AI to go through the large amounts of unstructured and structured data makes it perfect for accurate and timely prediction of the potential risks. This in turn helps the financial institutions to work in a proactive manner to reduce the potential losses associated with those risks.
Financial institutions are more susceptible to fraudulent activities than any other industry. The world is transitioning from cash to digital money. Now, customers use online transactions for shopping, paying bills, and receiving or transferring money. All these daily chores can be accomplished with ease by using smartphone applications.
These institutions spend huge amounts of resources and have separate departments which are just concerned with the identification and prevention of frauds. They cannot afford to be negligent when it comes to keeping a close eye on every operation to ensure transparency. The digitization of financial operations has given rise to new sets of challenges in the form of cybersecurity attacks. Though every industry is facing a challenge of intensified cybersecurity attacks since the last few years, however the way these attacks threaten financial institutions is unique.
Artificial Intelligence is playing a critical role in ensuring the security of online transactions by detecting odd behaviors because the AI finance system learns from the volume of past transactions and alerts the system, if any anomalous transaction occurs. An example of these abnormal behaviors includes the user using a card from one geographic location and just after a few minutes the card is used elsewhere or withdrawing an outlandish amount of money from an account. Such suspicious transactions are identified by devices powered by Artificial Intelligence because the past data shows that a user can’t reach another geographic location within a few minutes or the previous transaction history of the user depicts that he/she has never withdrawn this amount of money. Since AI finance systems learn continuously, therefore there is a possibility that they identify an activity, even when a regular and authentic transaction is taking place. In this case, humans can intervene and allow the transaction to take place. The stimulating aspect of the AI system is that it learns from the incident and does not raise a red flag on that kind of transaction in the future.
Digitizing Paper Records
Financial institutions have a mammoth amount of historical data in the form of paper records. These records are often stored in physical locations such as cabinets, drawers, or other storage rooms. We all know about the qualms of searching these paper records for extracting essential information. Besides the chaotic nature of these records, they are more susceptible to losses or damages than digital records. The confluence of all these factors demands a call of action to digitize all the paper records for ease of use.
However, manually digitizing volumes of paper records is quite challenging, especially if the institution is in business for decades. AI-powered devices require historical data to get trained for making effective decisions. The training process of Artificial Intelligence finance devices is compromised if they do not have access to large historical data. Hence, to fully utilize the decision-making capability of AI, financial institutions need digitization of the paper records. Interestingly, Artificial Intelligence also extends its helping hand in digitizing these paper records. Instead of manually entering the data, employees can scan the document and upload it into the software. These software packages can extract all the information from the document because they are trained by AI to recognize the written characters. Some of the AI driven technologies to digitize paper records are explained below:
- Optical Character Recognition (OCR): Financial institutions can use the OCR software to automatically extract information from the scanned documents.
- Intelligent Character Recognition (ICR): It uses advanced OCR technology and is ideal for digitizing raw handwritten documents. The current accuracy that this technology offers is between 50% to 70%, however, its encouraging aspect is that the software packages that use this technology enhance their accuracy levels as they are used.
Automated Financial Advisory
With the integration of AI in the financial industry, we are witnessing the prevalence of robo-advisors. Robo-advisors employ algorithms to give financial advice to people with little or no human intervention. Robo-advisor works by using an online survey to gather data about the client’s financial condition and future objectives. It then processes this data to give financial advice to the clients and invest their assets. Besides accurate and data-driven advice, an added benefit of robo-advisor is that they help to save cost and time. We can also combine human advisory with machine calculations to offer accurate future insights to the client. This collaboration of humans and machines in the advisory domain is known as bionic advisory. Robo-advisory has been in place since 2008, while the bionic advisory is an emerging field. This shows that the role of human financial advisors will not be eliminated in the future, because the best financial advice combines human insight with Artificial Intelligence.
Insurance Claim Automation
Insurance claim automation using AI is still at its budding phase. It is predicted that the insurance companies will fully automate their claim management cycle using AI-powered computers in the future. AI can offer innumerable benefits to the insurance companies which will be discussed in this section in detail.
AI-based software can help to automate the insurance claims process and mitigate the risk of overpayment. Hundreds of customers claim insurance from insurance companies every day. The insurance companies have an internal mechanism to process these claims, however, due to the overwhelming number of people claiming their insurances, the process may get sluggish at times. Besides the slow insurance claim process, due to many insurance claims, insurance companies may find it hard to recognize important patterns in the claim data. AI-powered systems can optimize the claim process by reducing the time taken to process each claim and recognize patterns in the claim data. Some companies are building software for insurance companies that combine the powers of AI and predictive analytics to mitigate the instances of overpayments. AI-powered software can detect the anomaly in the payment behavior of the insurance company based on past data. For example, the insurance company might be paying a higher amount of money to the customer for a claimed insurance than it had paid to other customers with similar circumstances. To detect this kind of abnormal behavior, AI-powered software must learn from the large amounts of historical data, either from a single or many insurance firms.
Insurance companies face tremendous costs in lieu of car insurance claims every year. After the client makes a car insurance claim, the experts from the insurance firm visually analyze the car and prepare a summary of their findings. This process makes the whole insurance claim procedure not only time-consuming but also prone to an inaccurate analysis by the experts. To avoid this, car insurance companies can employ AI-powered software and applications to restructure the entire process. Deep learning algorithms can estimate the repair costs of the vehicle with great accuracy and at a much faster pace than the experts who manually analyzed the damaged vehicle. These algorithms are trained by feeding different images of damaged vehicles, in different conditions. The algorithms learn from these images and when they are exposed to real-life imagery of a damaged car, they accurately predict its cost.
The huge amount of unstructured data in banks makes information retrieval quite challenging for the employees. To optimize daily operations, there is always a need for a more sophisticated system that can automatically search through a large database of structured and unstructured data and summarize the relevant information. This kind of automated system also helps financial analysts to make timely and informed decisions. AI-powered systems can explore the large data and can determine what is important and what is not based on its experience. It can then condense the relevant information and present it before the authorized individual succinctly. Employees working in financial institutions can type the keywords in these systems to search the specific information.
AI-powered summarization applications identify the information from various sources, remove the unnecessary data, summarize the data, order the content into a hierarchical structure, and rewrite the information so that the end-user can comprehend it easily.
Improved Customer Service
Using chatbots, financial institutions can enhance customer experience and reduce the load on the customer service department. A chatbot is an online AI-powered application that communicates with customers through text or text-to-speech. These chatbots usually serve as the first point of contact and try to resolve the customers’ issues. If the issue is too complicated for the chatbot to understand, then the customer is referred to a human employee. Chatbots not only save time and cost resources associated with maintaining a bigger customer service department of a financial institution, but also save the time of the customer. The historical data of the customer support department is used to train the chatbots so that they can efficiently solve the frequent issues faced by the customers. The chatbots have a limitation that they cannot provide out of the box solutions to the customer’s problem because they need examples of customers’ issues to respond towards a new problem accurately. Human employees can train the chatbots continuously by correcting the system when it inaccurately identifies a customer’s issue.
ATM stands for “Automated Teller Machine” that allows customers to withdraw cash and perform basic financial transactions themselves without the need of a bank representative. Banks install a network of ATMs across many geographical locations for the convenience of the customers, especially in the remote areas. Managing ATMs in faraway places is challenging for the banks, since they need human employees to perform the maintenance tasks. Predictive maintenance of ATMs is gaining momentum because it helps the banks to determine the breakdown of an ATM beforehand. In this way, they can timely react to solve the issue and perform routine maintenance tasks, which in turn saves customers from nuisance. Without this technology, it is very difficult for a bank to determine which ATM will need maintenance or is likely to shut down. Hence, they end up losing the revenue from ATM fees to their competitors in that area, because customers search for the ATM of other banks, when one ATM is shut down.
You may be wondering how predictive maintenance works? Well, this kind of maintenance works when banks install IoT sensors to different components of their ATMs. These sensors use AI to predict which component of the machine will need repair. IBM offers such predictive maintenance software to optimize the maintenance operations of ATMs. The company claims that its software can predict the performance of an ATM at the component level, thus helping the banks to schedule the maintenance beforehand.
Undoubtedly, AI offers undeniable benefits to the financial industry. AI finance systems are automating the processes in the financial industry, and in the future, we may need little or no human intervention, while carrying out certain finance operations. This is quite encouraging because it will not only save a huge amount of financial resources but also the valuable time of the existing employees working in the finance industry. Consequently, they can invest their time in improving the customer experience and making financial transactions more reliable and trustworthy.