Finance is something that no person on earth can live without. It is the basic necessity of life, as everybody needs money to eat, travel, and buy things. Although as technology gets smarter so do people. The present financial market is already comprised of humans as well as machines. People are finding more and more ways to default on loans, stealing money from others account, creating a fake credit rating etc.
Today, machine learning plays an integral role in many phases of the financial ecosystem. From approving loans, to managing assets, to assess risks. Yet, only a few technically-sound professionals have a precise view of how ML finds its way into their daily financial lives. Nowadays, detection of frauds has become easy thanks to Machine Learning. Given the fact that machine learning is a very broad concept, we will learn a few ways how Finance could benefit with the use of Machine Learning.
Ref – https://www.cbinsights.com/blog/google-apple-microsoft-ai-patents/
Machine learning is the science of designing and applying algorithms that are able to learn things from historical data. It was born from the aspects of pattern recognition ML explores the study. And construction of algorithms that can learn from and make predictions on data. This allows ML programs to respond to different situations even though not being explicitly programmed. ML has given us ample amounts of use cases like self-driving cars, product recommendation engines, predictive analytics, speech recognition to name a few. Increasing reduction of human effort is the main aim of data scientists with implementing ML. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. It wants to bring down the time that humans take to read, understand, analyze the big data to a few seconds.
The two most widely adopted methods for implementing ML are supervised learning and unsupervised learning. Supervised learning algorithms are trained using labeled examples, such as an input where the desired output is known. And in Unsupervised learning no labels are given to the learning algorithm, leaving it on its own to find structure in its input.
As our human brain limits our thinking to a certain number as compared to that of machines. We can at maximum concentrate only on 3-4 things at the same time, whereas machines can concentrate on several thousand. Some of the reasons why we should use machine learning in Finance are:
Reliability: When it comes to handling finance, establishing trust on the person is essential. Banks, investment firms, stock markets do not transact a few dollars every day. They transact in high quantities like billions of dollars. Hence, it is imperative that we have trust in the firm or person handling it. As people can be biased and selfish, some people tend to do fraud with the money they are handling. In order to cope with these issues, machines embedded with ML are corruption free and complete the requests provided.
Speed: We all know it is very difficult to trade stocks in the stock market. People usually carry out a lot of analysis from historical data, plot graphs, and use formulas to predict the future of the stocks. Some just randomly place bets against the market. All this looks and sounds awfully hectic and is very time-consuming. Machine Learning algorithms are able to provide accurate in-depth analysis of thousands of datasets. And also give concise and accurate predictions within a fraction of a time. It helps alleviate the hassles of going through big data and making sense of what it means.
Security: With the recent hit of WannaCry ransomware attack all across the world, it has become clear that we are still prone to hacking and cyber security theft. Machine Learning categorizes its data over three categories. Then builds models which are an essential step in predicting the fraud or anomaly in the data sets. Whereas manual reviewing is costly, time-consuming and leads to high false negatives which are unacceptable in the financial industry.
Accuracy: People do not have the ability or do not like doing the same mundane task repetitively. And if they do, it is followed by many errors. Moreover, machines can perform repetitive tasks for an infinite amount of time. ML algorithms do the dirty work of data analysis and only escalate decisions to humans when their input adds insights. ML are often more effective than humans at detecting subtle or non-intuitive patterns to identify fraudulent transactions. Also, unsupervised ML models can continuously analyze and process new data and then autonomously update its models to reflect the latest trends.
Very often we see multinational corporations defaulting on payment of their debt to the banks. Even after being extremely careful and verifying the credibility of the corporations, this seems to be a very common problem in the financial sector. Some financial institutes use scoring models to lower credit risk in credit appraisals, and in the granting and supervision of credit. Credit scoring models based on classical statistical theories are widely used. However, these models are less resilient when it comes to large amounts of data input. And as a consequence, some of the assumptions made in the classical statistics analysis fail to hold true. This, in turn, influences the accuracy of prediction.
Identifying the credit risk score of customers based on their nationality, occupation, salary, experience, industry working in, credit history etc is very critical to the banks. All this even before providing any service to the customers. This is an important Key Performance Index (KPI) for the banks before providing credit or any other products related to finance.
Introducing a central, integrated finance & risk mechanism which can be ‘instantly’ used for a customer is a major challenge these days. Even now, banks fail to produce an instant loan approval due to their inability to predict the risk score of a customer. ML could fasten the process of granting credit to customers and avoid the due diligence required that eats a lot of time.
To identify the credit score of the customer, we make use of regression algorithms. These algorithms use a statistical process for estimating the relationships among variables. Regression analysis helps understand how the typical value of the dependent variable changes when any of the independent variables is varied while having the independent variables fixed. These algorithms are widely used in forecasting and prediction, where its use has been accelerated with the field of machine learning.
The first step in this approach is defining the population, i.e., the availability of credit history of the customers. Then selecting the people at whom this is aimed and defining the benchmark for satisfactory and unsatisfactory performance. This part would serve as the base dataset for the regression algorithm to start its operation.
In the next step we select the sample, for which the following criteria are applied:
Among the possible pieces of information selected, also called demographic variables are gender, age, profession, company, education, marital status etc. We generally suggest that the clients in the sample be for a period of 12-18 months. This amount of time is sufficient to check for delays in payments and defaults. And then we consolidate the payment behavior of a good client.
Now we begin carrying out a preliminary analysis by choosing variables to put into the modeling, grouping attributes of variables and creating dummy variables. By using contingency tables, a calculation is made of the relative risk (RR) associated with the levels of the independent variables. Done by dividing the percentage of good clients by the percentage of bad ones for each level. The more the percentages of good and bad clients differ for the levels of a single variable, the greater the usefulness of this variable for the prognosis of future performance. Generally, the RR lies between 0 and 2 while 0 being Extremely Poor and 2 being Excellent.
Levels classified as neutral are not used in the analysis since they are not greatly different from the good and bad groups.
The construction of the model comprises of choosing the multivariate statistical technique. Then determining the software to be used, selecting the independent variables and checking assumptions of the techniques. Once the data has been reduced to cluster levels we use the discriminant analysis, logistic regression and neural networks. Discriminant analysis and logistic regression are statistical techniques that take different approaches. Resulting in the possibility of which one of these techniques succeeding when the other fails. Neural networks are part of the process as it has the ability to deal with nonlinear and discontinuous effects. The software to be used should be checked regarding analysis to be performed and easiness of use.
Finally, in order to evaluate the performance measures, we find the KS test for two samples. The KS test has the characteristic of simplicity. What we are looking for is the difference between two clusters i.e good payers and bad payers translated by their respective result forecast. The differences between the distributions of good and bad payers for each forecast are determined. And the value of the KS test is the greatest of these differences in the module. As the final result obtained from the model is usually a scale from 0-1, a client is defined as a poor payer when the result is less than 0.5; otherwise, he/she will be classified as a good payer.
Ref – https://www.fairwinds.org/inside-fairwinds/fraud-protection-center/report-fraud.html
Fraud detection process using machine learning starts with gathering and segmenting the data into three different segments. Then machine learning model is fed with training sets to predict the probability of fraud. These datasets are found from historical data. Lastly, we build models as an essential step in predicting the fraud or anomaly in the data sets. The detection of fraud now takes up much less time as compared to traditional detection. Since the use of machine learning is still small and growing it will in a few years evolve a lot more and be able to detect complex frauds.
Ref – http://updatedigital.at/news/marketing/iab-impulse-zum-thema-real-time-advertising/73.255
All of us have heard of people become billionaires through trading stocks. But it is very tough to beat the market without having sound knowledge of how it works and the current trends. Well, now with the use of machine learning stock prediction has become fairly simple. These machine learning algorithms make use of historical data about the company like balance sheets, profit and loss statements etc. And finally, analyze them to find out meaningful signs regarding the future of the company. Further, the algorithm can also hunt for news about the company. And learn from sources around the world regarding how the market feels about the company. Also, with natural language processing, it can scan through the news channels and video libraries of social media to find more data about the company. As this technology is still developing and not accurate enough. It is safe to say that in the near future it will be able to make very accurate predictions of the stock market.
CRM is very prominent in Retail Banking Space. When it comes to Treasury space within banking, customer relationship management hardly exists. Treasury has a diverse product palette such as FX, Options, Swaps, Forwards and more importantly Spots. Having an online transaction by combining product sophistication of these, risk aspects of customer, market and economy behavior and credit history is almost a distant dream for banks.
Ref – http://www.asktrim.com/
Chatbot can be programmed as a Financial Advisor. A finance bot can be your personal financial guide giving you advice on anything from investing your money in properties to buying a new car. This bot can be a life-saver and the first step to organizing your life. A finance bot can also turn complex finance terminology into layman’s language, saving your time and reducing your pain. It could help you track your expenses and help you save money. Kasisto’s AI platform is going to power a mobile-only bank in India, where all kind of customer requests will be handled by Chatbots.
Tasks such as Customer Alert, Money Transfer, Deposits checks, Inquiries, FAQs and search, Content Delivery Channel, Customer Support, offers, etc can be easily achieved by banking bots. It can provide a running tally of the potential savings that you are accruing by documenting your deductible expenses. Tax Bots can also provide Tax tips, it can be integrated with your banks, and what not.
In conclusion, although machine learning is a newer technology there are lots of academicians and industry experts among which machine learning is very popular. It is safe to say that there are a lot more innovation coming in this field. And adopting Machine Learning also has its own setbacks due to data sensitivity, infrastructure requirements, the flexibility of business models etc. But the advantages outweigh the drawbacks and help solve lots of problems with Machine Learning.
Since machine learning techniques are far more secure and safer than human practices, it is the best choice for finance. It would help provide opportunities to banks and other financial institutions by helping them avoid huge losses caused due to defaults. Finance is a very critical matter in all the countries around of the world, and safeguarding them against threats and improving its operations would help all grow and prosper faster.
Artificial Intelligence and Machine Learning - 9 MIN READ
Fintech changing financial services - Innovative or Disruptive?
Artificial Intelligence and Machine Learning - 11 MIN READ
12 Use Cases of AI and Machine Learning In Finance