Artificial Intelligence is the future of Finance

Artificial intelligence was founded as an academic discipline in 1955. It was created on the assumption that human intelligence "can be so precisely described that a machine can be made to simulate it". 

Human has limited brain power and limited time. On the contrast AI has lot more resources like computational power. AI uses different type of algorithms which are capable of self-learning from data pattern or features in the data; They can enhance themselves by learning new strategies. Also, these algorithms can be so powerful that it can write a new algorithm based on changing situation.

With a changing technology AI is reshaping the financial sector by its own way. Every day we can see new features, new technology in all kind of Digital Assistant apps. This is making AI as a strong competitor to any technology. We can see now a better customer care which uses a self-help VR system which is nothing but an intelligent natural language processing technique with mix of high-end speech technology.

AI has multiple benefit in finance industry starting from credit decision, personalize banking, risk management etc. Also, it helps to automate middle-office support by providing 24/7 customer interactions, reducing the repetitive work etc. According to an article from businessinsider, automating middle-office tasks with AI has the potential to save North American banks $70 billion by 2025. Further, the aggregate potential cost savings for banks from AI applications is estimated at $447 billion by 2023, with the front and middle office accounting for $416 billion of that total.

From data to DECISION; AI driven credit decisions help bank and credit lender make smart choice by analysing large amount of data and multiple factors like current income, employment, credit history and ability to earn in addition to older credit history. It provides faster, non-biased and more accurate assessment. Credit Scoring technique is nothing but an intelligent AI system which is running on complex rules. Those rules which is running in backgrounds distinguish between a high risk and low risk applicant based on analysing all the credit history in past. AI can also adapt to new problems, like credit card churners, who might have a high credit score, but are not likely to be profitable for the card issuer. Or an applicant with a stable job income but might not be a good candidate for high amount of loan based on all his previous dealings.

Based on advance rule AI can help customer who has good credit risk but getting denied based on manual/rule-based history check. One negative aspect can be, accessing all kind of previous history or analysing a large amount of personal data can lead to a privacy concern.

AI model has improved the analytical capabilities in risk management by its processing power of huge amounts of structured and unstructured data in a short period of time which cannot be done by a human. Algorithms analyse the past details for risks and identify what can be the potential issues. This allows risk managers to identify any risks associated with any action and that gives the idea how to prevent it.

With old risk management tool, it was very difficult and time consuming to analyse the real time activities and identify the potential risks. New tools with AI is helping to analyse all kind of risks and come up with probable solution,  although it has some technical challenges of developing AI apps for banking, such as building correct and relevant algorithms, there are also challenges related to the regulatory field and data access rights.

AI is a perfect match for the rapid escalation of nuanced, highly sophisticated fraud attempts. AI approach to fraud detection has received lot of publicity in recent times. Also, this has successfully shifted industry's old rule-based approach to ML based solution. Using rule-based approach it was almost difficult to find hidden and implicit correlations in data. ML based approaches provides the features like automatic fraud detection technique based on data and this is possible in real time. New approach provides fraud analysts with real-time risk scores and greater insight into where best to set threshold scores to maximize sales and minimize fraud losses. 

Due to rise of mobile payment, all banks have introduced various verification stages so that it can handle modern frauds and scams. AI algorithm can help in paper based and bank data reconciliation which eliminates human error. Intelligent AI algorithm can help a bank to understand or send a notification based on transaction done away from customer's location. Another common scam is when scammers use someone's personal information. AI can help to prevent such case to and inform individual for e potential data theft.

AI can be used for anti-money laundering. Different countries had different guidelines while operating bank can be same. So banks or investment firms has to deal with different regulations to identify any suspicious activity. Rule-based approach sometime fails to identify the risk. 

AI has brought a significant change in trading. Electronic trades account for almost half of the total revenues from cash equity trading. Most companies such as hedge funds, use AI-powered analysis to get investment ideas and build portfolios. This kind of trading is spreading quickly across the globe.

Trading system which is built on advanced AI can monitor any kind of structured data like DB or sheets and unstructured data source like news or social media etc. Based on market news or social media follow up or simply analysing large data it can give the right direction to any trading platform.

AI Trading gives benefit over Algo trading. Algo trading is whereby a computer program follows a set of instructions set to execute a trade. AI trading, on the other hand, is whereby machine learning is used to observe, study and analyze market conditions, trading patterns, and data, then predict what will happen.

Banks and financial service providers were challenged to provide a best customer service in digital world. New technology is helping customer to do most of the banking task virtually. AI driven chatbots are helping customer with their queries which has helped to reduce call center workload. Now a virtual agent can help and guide you if you want to open a bank account or pay a bill.

AI helps to understand customer behavior which helps the bank to customize the services or production by adding customized features. Based on all the transaction done by customer with bank, AI can suggest bank what can be the best reward program for a customer. Reward program will help bank to retain customer and based on good services received, one customer can help bank get new member by referring them. Sometime financial institutes are using automated virtual system for any market research related work, as per market survey, people are more comfortable and honest while interacting with non-human entities like chatbot.

Automation is one of the key aspects in Banking. By adapting robotic automation process many industries have cut down their operation costs. Automation of digital and physical task helped to boost the productivity also.

RPA is least expensive and easiest to implement which serves better than old business process. Example: it can read thousands of emails, letters or agreement or any legal document and identify the right keyword and stores in database for further business process. It helps to avoid any human error which is very important for any banking sector. An article from Forbes says, more than 65% cost reduction reported by Ernst & Young by adapting automation and this significant milestone has been identified as as "Gateway Drug To Digital Transformation". 

The global payments landscape is going though a massive change worldwide. Every person in world has a smart phone along with a bank account or wallet which is helping customer to transfer the money across globe from anywhere and anytime. Now for this customer needs a seamless framework along with better rate and high security. Here AI is helping the leading remittance companies in market to provide better exchange rate, handle the risks or provide a secure transaction channel to every customer in world. There are tools based on AI, which can tell a customer about comparison of exchange rates provided by different remittance company. 

Now the next question comes in mind, what if the money transferred using new technology goes into wrong hands. Answer to this question is identify the right pattern to protect the money to prevent any organized crime. In earlier days any financial institute used to use the hard-coded rules to identify any suspicious behavior. But there was no process to identify any number of patterns. Example: if banking software finds a large number of amounts was sent from person A to B then it used to raise flag. But what about small amounts sent multiple times to one account or multiple accounts across many countries. Banks are increasingly turning to machine learning to mine vast quantities of bank data and find anomalies in accounts and transactions that might otherwise have gone unnoticed. 

Most automated transaction monitoring systems can identify transactions that are related to terrorism financing by using watch lists. To identify the "unknown" financier of terrorism financial institutes are using a different search strategy for detection. For example, using other relevant information from other customer channels combined with data could help an institution to better identify suspicious behavior.