How Machine Learning Has Disrupted Finance Industry
In this article, see how machine learning has disrupted the finance industry.
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In the last decade, the finance industry has seen an infusion of cutting-edge technologies like never before. This transformation is largely attributed to many startups that appeared on the scene post 2008 recession and followed a technology-first approach to create financial products and services with a target to improve customer experience. FinTech, as these startups are known, have been the early adopters of the new technologies like Smartphones, Big Data, Machine Learning (ML), Blockchain and were considered the trendsetters that were later followed by more traditional banks and financial institutes.
The recent advancements in machine learning and deep learning has really pushed the boundaries of computer vision and natural language processing. FinTechs are leaving no stones unturned to capitalize on these breakthroughs to improve financial services. As per a report, the ML Fintech market was valued at $7.27 billion in 2019 and it is expected to grow to $35.40 billion by 2025. Statista forecasts that the entire banking industry overall will be able to derive the business value of $182 billion globally with machine learning by the year 2025.
These numbers are indeed mindboggling and show how much this industry is already reaping benefits from the machine learning. Let us take a deep dive into examples and real case studies of machine learning disruption in the finance industry.
1. Risk Management
Due to the nature of the industry, Finance is always prone to various kinds of risk. If these are not properly managed it can cause trouble both for the financial institutes like banks and the customers and in the worst case may also lead to complete bank collapse. There multiple types of risks that always loom on the banks and the most common ones are credit risk and market risk. Most of the banks have now started to leverage artificial intelligence to minimize such risks.
Banks now assess the creditworthiness of the loan applicants by machine learning predictive models to find the probability that they might default or not in the future. Those with a high probability of default are not given any credit thus reducing bank loss from default loans. Zest Finance is a leading FinTech startup in this area and it has managed to reduce the default rate by 20% by using machine learning analytics.
The stock market is always seen as a very risky prospect because it can crash when you least expect leaving people and portfolio managers clueless. However, no crash is sudden, in fact, there are many micro and macro factors that lead to the crash but humans are unable to pick up these clues in advance. Machine learning and Time series models can be used to predict these patterns well in advance to take timely action before a crash. Trading Technologies and Kavout, are two prominent companies that are working in this area to identify complex trading patterns using machine learning.
There is another interesting approach taken by a company EquBot which uses IBM Watson to scrape various news and social media posts of the market and create a sentiment analysis of the market to predict the trends.
2. Fraud Management
Banks and insurance companies all over the world face regular attempts of financial fraud which results in huge losses. In the US alone, in 2019, the insurance companies faced a loss of $34 billion due to fraud claims. These fraudulent claims can be detected efficiently by machine learning classification models. A Turkish insurer AKSigorta uses its predictive model that can flag a suspicious claim within 8 seconds for further scrutiny, this has helped them to increase the detection of such false claims by 66%.
Transactions from stolen credit cards and compromised bank details result in huge losses to banks and customers. To curb such frauds many companies are building ML-based fraud detection systems to detect real-time fraudulent transactions. When such systems see an anomalous transaction it either blocks it or seeks customer confirmation by OTP. One such company Datavisor claims that its machine learning can detect 30% more financial frauds with 90% accuracy.
Every day millions of people receive phishing emails and thousands of people fall prey to them by giving away their financial details that lead to financial frauds. Now many prominent email service providers have integrated machine learning classification systems to detect such phishing emails and block them. Gmail alone blocks 10 million spam and malicious emails every minute thus drastically reducing the chances of phishing frauds.
It is very important for banks to implement robust security and they are now using smart surveillance cameras to keep a tab on activities both on-premises and on remote ATMs. These surveillance cameras are powered by computer vision and IoT technologies that can detect suspicious activities and raise an alert. Uncanny Vision is one such leading company that provides such AI-based surveillance cameras for ATMs.
To safeguard the on-premises security banks are also relying on biometric securities such as fingerprint, retina, facial scan to authenticate the person on their premises and prevent unauthorized people to access restricted areas. Some of the popular banks that have implemented biometric securities include RBS, Wells Fargo, Bank of America, Barclays.
In fact, it is not only for on-premises security, but biometric features are also being used to authenticate the customers who access banking services via their smartphones. It adds an extra layer of security on top of the password to make sure the right user is using their mobile app.
4. Customer Experience
Winning over customers by giving them a great experience ensures that they keep on returning to use your banking service for almost their lifetime. Traditionally banks had not been a very user-friendly arena, but it started to change gradually during the age of online and phone banking. Banks are now trying to take this experience to another level by leveraging machine learning.
One of the most tedious jobs while applying for a bank account is the KYC process which is considered as overhead by the customers and delays the opening of the account. There are now efforts to reduce the onboarding time of customers by automating the KYC process with the help of OCR and computer vision technologies to process customer documents faster. In fact, European bank Banco Bilbao Vizcaya Argentaria (BBVA) has simplified the KYC process so much that customers can simply upload selfie photos or videos to open their accounts without much hassle.
Once customers are onboarded it is important to take care of their regular inquiries and provide assistance when needed. In recent years banks have successfully used ML-based chatbots and virtual assistants on their website and mobile apps to provide on-demand help to the customers. According to one report, by 2022, banks can automate up to 90% of their customer interaction using chatbots. Bank of America is one of the banking giants that has already launched a virtual assistant, Erica, to help with the customer queries.
Banks are also leveraging machine learning to build intelligent robo-advisors to give customers more personalized financial advice which is not only beneficial to them but also increases the probability of conversion for banks. It is a win-win situation for both customers and banks.
Machine learning is indeed the future where the world is heading towards and this is just the beginning. If what we discussed here already impressed you we can surely say that the finance sector will see more innovative adoption of ML in the near future that we can’t even think of today.
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