Machine learning is one amongst those technologies that is invariably around us and that we might not even comprehend it. For instance, machine learning is employed to resolve issues like deciding if an email that we got is a spam or a genuine one, how cars can drive on their own, and what product someone is likely to purchase. Every day we tend to see these sorts of machine learning solutions in action. Machine learning is when we get a mail and automatically/mechanically scanned and marked for spam within the spam folder. For the past few years, Google, Tesla, and others have been building self-drive systems that may soon augment or replace the human driver. And data information giants like Google and Amazon can use your search history to predict which things you are looking to shop for and ensure you see ads for those things on each webpage you visit. All this useful and sometimes annoying behavior is the result of artificial intelligence.
This definition brings up the key component of machine realizing specifically, that the framework figures out how to tackle the issue from illustration information, instead of us composing a particular rationale. This is a noteworthy advancement from how most writing of computer programs is done. In more customary programming we deliberately examine the issue and compose code.
This code peruses in information and utilizes its predefined rationale to distinguish the right parts to execute, which at that point creates the right outcome.
Machine Learning and Conventional Programming
With conventional programming, we use code structs like– if statements, switch-case statements, and control loops implemented with — while, for and do statements. Every one of these announcements has tests that must be characterized. And the dynamic information, typical of machine learning issues can make defining these tests very troublesome. In contradiction to machine learning, we do not write this logic that produces the results. Instead, we gather the information we need and modify its format into a form which machine learning can use. We then pass this data to an algorithm. The algorithmic program analyses the data and creates a model that implements the solution to solve the problem based on the information and data.
Machine Learning: High-Level View
At a high level, machine learning could be understood in a way as shown in the following diagram:
We initially start with lots of data, the data that contains patterns. That data gets inside machine learning logic and algorithm to find a pattern or several patterns. A predictive model is the outcome of the machine learning algorithm process. A model is typically the business logic that identifies the probable patterns with new data. The application is used to supply data to the model to know if the model identifies the known pattern with the new data. In the case that we took, new data could be data of more transactions. Probable patterns mean that a model should come up with predictive patterns to check if the transactions are fraudulent.
Machine Learning and FinTech
FinTech is one of the industries that could be hugely impacted by machine learning and can leverage machine learning technologies to get better predictions and risk analysis in finance applications. Following are five areas where machine learning could impact finance applications and so financial technologies can become smarter to take care of fraud detection, algorithmic trading or portfolio management.
Applying predictive analysis model to the huge amount of real-time data can help the machine learning algorithm to have command over numerous data points. The traditional method of risk management worked on analyzing structured data against some data rules which were very constrained to only structured data. But there is more than 90% of data that is unstructured. Deep learning technology can process unstructured data and does not really depend upon static information coming from loan applications or other financial reports. Predictive analysis can even foresee the loan applicant’s financial status that may be impacted by the current market trends.
Internet Banking Fraud
Another such example could be to detect internet banking fraud. If there is a continuous fraud happening with the fund’s transfer via internet banking and we have the complete data, we could find out the pattern involved. Through this, we can identify where are the loopholes or hack prone areas of the application. So, it’s all about patterns and predicting the results and future based on those patterns. Machine learning plays an important role in data mining, image processing, and language processing. It cannot always provide a correct analysis or cannot always provide an accurate result based on the analysis, but it gives a predictive model based on historical data to make decisions. The more data, the more the result-oriented predictions that can be made.
One of the areas where machine learning can play an important role is sentiment analysis or news analysis. The futuristic applications on machine learning can no longer depend upon the only data coming from trades and stock prices. As a legacy, the human intuition of financial activities is dependent upon trades and stocks data to discover new trends. The machine learning technology can be evolved to understand social media trends and other information/news trends to do sentiment or news analysis. The algorithms can computationally identify and categorize the opinions or thoughts expressed by the user to make predictive analysis. The more the data the more accurate would be the predictions.
Robo-advisors are a kind of digital platforms to calibrate a financial portfolio. They provide planning services with least manual or human intervention. The users furnish details like their age, current income, and their financial status and expect from Robo-advisors to predict the kind of investment they can make, as per current and futuristic market trends to meet their retirement goals. The advisor processes this request by spreading the investments across financial instruments and asset classes to match the goals of the user. The system works on real-time modification in user’s goals and current market trends and does a predictive analysis to find the best match for the user’s investments. Robo-advisors may in future completely wipe out the human advisors who make money out of these services.
The highest concern for banks and other financial institutions is the security of the user and user’s details, which if leaked could be prone to hacking and eventually resulting in financial losses. The traditional way in which the system works are providing a username and password to the user for secure access and in case of loss of password or recovery of the lost account, few security questions or mobile number validation is needed. Using AI, in the future, one can develop an anomaly detection application that might use biometric data like facial recognition, voice recognition or retina scan. This could only be possible by applying predictive analysis over a huge amount of biometric data to make more accurate predictions by applying repetitive models.
How Can Magic FinServ help?
Magic FinServ is aggressively working on visual analytics and artificial intelligence thereby leveraging the concept of machine learning and transforming the same in the perspective of technology to solve business problems like financial analysis, portfolio management, and risk management. Magic FinServ being a financial services provider can foresee the impact of machine learning and predictive analysis on financial services and financial technologies. The technology business unit of Magic uses technologies like Python, Big Data, Azure Cognitive Services to develop and provide innovative solutions. Data scientists and technical architects at Magic work hand in hand to provide consulting and developing financial technology services having a futuristic approach.