Applications of Machine Learning in Fintech sector
Financial world presents a lot of opportunities to apply various machine learning techniques. Financial world is based on quantitative figures and statistics which is perfectly suited as a use case for data analytics, a sub field of machine learning. Even highly volatile and not easily interpretable phenomenons, even for human experts, such as market analysis and future prediction, machine learning models such as text/sentiment analysis, semantic analysis can come to rescue.
High Frequency Trading:
High frequency trading (HFT) also known as algorithmic trading is the use of sophisticated computer algorithms and advanced electronic tools to fuel quantitative trading for high traffic, speed and turnover by setting up an automated pipeline with some sort of ‘human intelligence’ supervision. As trading and market continues to grow it’s only reasonable and to the best of interest of financial industry that we employ some sort of automation pipeline. Based on various kinds of data from history we can have ML models trained for this exact purpose. In order to ensure accidental oversights and errors there can be human experts to approve/disapproved decisions made with confidence lower than the threshold.
Fraud Detection:
Detecting fraudulent and anomalous activities in financial sector is very crucial and equally difficult to accomplish too. Difficulty lies due to the fact that the wrong players come up with newer and newer tactics to game the system. Having a pre set rules to accomplish this task is simply not going to work in the long run because of the sheer dynamicity of the problem in hand. Having a constantly learning ML model which keeps changing and adapting to the new data, preferably some sort of reinforcement learning variation, can help solve this problem.
Robo advisers:
Financial planning is a very critical task not just for organizations and corporations but even for individuals. Keeping track of finances, making decisions regarding investment and trading require a detailed knowledge and analysis of what’s going on in the fintech world. The growing magnitude of data and information in this segment is simply impossible to be reckoned with for human analysts. This is where the concept of robo-advisers has sprung up in the recent years where machine learning is used for financial planning.
Loan underwriting:
Loan underwriting is the process of assessing the risk before sanctioning loan or mortgage to an individual by the banks. Again, this is a huge domain. There could be literally millions of applications and to determine which one is eligible and which one is not is not an easy task. In order to evaluate such applications we can train a ML model against a dataset which contains various parameters of the applicant and the risk involved. Assisted with human intelligence this gigantic problem can be greatly simplified even to determine creditworthiness of insurance policy, regulatory and compliance risk assessment for individuals as well as organizations.
Chat bots:
Some sort of customer interaction is sought after just about in any industry dealing with finance. Most of these can now be handled using engaging conversational chatbots. These are guaranteed to be more factually correct (considered trained with proper data) and are less error prone because various human factors of judgement and prejudices are eliminated. Now with advance speech synthesis and improved human computer interaction accessing financial data and insights is easier than ever which gives a leverage to each individual to make better financial decisions.
Emergent Behaviour (Dealing with uncertainty):
In finance and economics there are a lot of emergent behaviours that cannot be explained clearly. Stock market is a perfect example. There is no central planning in place to direct stock prices. It’s a cumulative result of the decisions made by thousands of investors based on their limited knowledge and intuition. Mathematically modelling such behaviours is not possible and thus ML comes into play. There are chances that the machine learning model itself might not be very interpretable but overall employing ML models in such scenarios is guaranteed to yield far useful and better insights. Talking about emergent behavior, the world wide web (WWW) is another example. We never know to what direction the overall opinion is going to swing to. Based on social media platforms and news trends financial industries have to constantly keep up with their next strategy in order to maximize profit. There are already numerous firms for micro-targeting markets for advertisements and infamously even for electoral politics as revealed by the recent Cambridge Analytica Scandal. The legal and ethical standards behind such practices must be regulated and individual’s data should never be at stake. But for the most of the cases these principles work on anonymized dataset.
Originally posted at Udacity India
