In recent times, the finance industry has capitalized on the emerging technologies very rapidly than ever before. This is largely attributed to the rise of hundreds of young financial startups after the 2008 recession whose main object was the unification of technology with financial services to create a great customer experience. This new breed of financial startups known as FinTech completely revolutionized the financial industry in the 2010s. And soon the traditional banks and financial institutes also followed FinTech’s way to adopt emerging technologies to stay relevant in the competitive market.
Although there have been many technologies like 3G/4G, smartphones, Big Data, Blockchain, IoT, etc. that contributed to this revolution, one technology that really stands apart is artificial intelligence. There had been a resurgence of artificial intelligence in the 2010s mostly due to the success found in the research of deep learning, a branch of machine learning. This resulted in many industries including the finance sector to start investing heavily to integrate artificial intelligence capabilities into their operations.
In this article, we will see various areas where artificial intelligence has disrupted the finance industry.
Impact of AI in Finance
Banks sit on a huge pile of customer data which has insights on their transactions. They are exploring this data with the help of data science and visualization techniques to create meaningful insights for their leaders to make important decisions with respect to their operations and strategies.
Banks are leveraging machine learning predictive analytics to determine what are the chances a person who is asking for the loan may default in the future. If the default risk is too high the person is denied any loan. This helps banks in reducing their losses due to the huge number of loan defaults.
Read all about Artificial Intelligence.
Stock Market Prediction
Various financial firms are using time series modeling to predict the trend of the stock market and invest accordingly. Such models also help them to predict a market crash and take appropriate actions in advance to minimize the loss. Another area of innovation in this regard is the leverage of sentiment analysis from multiple online news portals for a particular company and predict the rise or fall in its stock.
Algorithmic trading is a practice of placing market orders automatically by computers that are programmed for that purpose only. Many programs have been built in the last few decades but the rise of artificial intelligence has made algorithmic trading more robust than before.
Financial companies use unsupervised learning techniques to understand their customer behavior and create multiple customer segments. This helps them to target specific customer segments with particular services or offers. This is a win-win situation for both customers who get targeted with valuable offers and for banks who get higher conversions.
Reduce Customer Churn
Customer churn happens when they jump companies for availing the same service. Losing any customer is very bad for companies. Finance companies are also very critical of losing their customers and they leverage predictive analytics to foresee customer’s chances of leaving them and they target these customers with more valuable offers to retain them. This is again a win-win situation for both banks and customers.
Fraudulent transactions have always been the most worrying aspects of risk management for banks. Machine learning has enabled banks to detect fraudulent and anomalous transactions in real time with more precision and prevent them at the right moment to avoid any loss. Now such capabilities are a core part of almost all transaction platforms of the banks around the world.
Anti Money Laundering
Money Laundering is the illegal movement of unaccounted money between two parties that may also be used for malicious activities like terrorism, especially in cross border money laundering. Similar to fraud detection, banks are also using machine learning to flag these illegal movements of money and stop it in a timely manner.
In the world of eCommerce and Netflix, users now seek personalized experience on all online platforms. Banks and Fintechs are using machine learning to create enriching personalized experiences for their customers on their websites and applications by recommending them offers that can be really valuable for users. For example, many investment portals have AI-enabled Robo-Advisors that recommend investment plans tailor-made for user’s needs and spending capacity.
Chatbots have become more sophisticated and better due to recent advancements in NLP. Banks are deploying chatbots on their website to help customers with most of their general queries. This helps them to serve customers more efficiently without keeping them waiting on customer support calls. For example, Bank of America has a chatbot, known as Erica, that helps the bank’s customer with queries related to transactions.
OCR – Optical Character Recognition
OCR is the technique of extracting characters from a scanned document and storing it in a computer-readable form. Banks have been using OCR for decades to process thousands of documents but the recent advancements in computer vision have improved OCR drastically giving banks the ability to process documents more efficiently.
Biometric security is based on authentication of biological traits of an individual like fingerprint scan, retina scan or face recognition scan. Such security is generally considered safer than other traditional means and has improved over the years due to advancements in computer vision and pattern recognition. Banking applications and their work-place are vulnerable to data or financial breach. This is why many leading banks are investing in biometric authentication for their employees and customers.
Fast Insurance Settlement
The process of insurance settlement can be very cumbersome due to manual processes involved in between. Insurance companies are leveraging artificial intelligence to automate many aspects of claim settlement. Some innovative FinTechs have gone to the extent of using computer vision technologies to assess the damage done to cars or houses by just going through the scanned images and approve their claims with minimal manual inspection in the overall process.
Elimination of POS
POS or Point of Sales is the terminal at retail outlets where the transactions are done by customers. In large retails the queue can be very long. Amazon has started cashiers Amazon Go stores where there are no POS. Customers can enter the store, pick the item and leave and they will be billed automatically from his Amazon Pay wallet. This futuristic intelligent store has been created by augmenting various facets of artificial intelligence. It is not very long when even other retail giants will start adopting such stores.
Artificial intelligence has been able to revolutionize the finance sector within a matter of half a decade. As we enter a new decade of the 2020s, it will be exciting to see how banks, especially the FinTech companies will leverage artificial intelligence to create more disruption in the industry.
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