Machine Learning is the new black, or the new oil, or the new gold! Whatever you compare Machine Learning to, it’s probably true from a conceptual value perspective. But what about its relation to finance, what situation we are having today?
Banks keep everything: a history of transactions, conversations with clients, internal information and more… Storages are literally bloated to tera-, and somewhere to a petabyte. Now, then, Big Data can solve this problem and process huge amounts of information: the greater the amount, the greater the detectable needs and behaviors of the client. Artificial Intelligence paired with Machine Learning allows the software learning the clients’ behavior and make autonomous decisions.
Sounds promising, but let’s check this out together and see what power Machine Learning and Big Data can bring to finance. Without further ado, let’s get started!
Things Machine Learning Can Do for Finance
# 1 Determine an Optimal Location for Bank
Information is 21st-century gold and ML & Big Data applications utilize it to render things that are really important for a client. And when it comes to financing endeavors, collecting information about every customer is a must thing to do. The most usual example of this is a process of doing regular operations at an ATM. The bank’s purpose is to process information received on all operations and on the next visit immediately execute a normal operation with a single button, without searching and numbers.
Information obtained using Big Data can be used to create and operate the engine, which determines the optimal place to open physical banks. The financial institution collects information about the most visited areas of the city, the time of visiting these areas, the stores to which their customers go, where the largest and smallest number of customers. Using this stuff you can easily select the most profitable location to open the bank (and not just a bank, by the way). You will agree this is a wonderful opportunity, cause location is very important for your future success.
#2 Find Best Solutions for Customer by Dint of Robo-Advisors
Robo-advisors are like virtual assistance, although they are not robots by their own sense. They are algorithms built to calibrate a financial portfolio to the goals and risk tolerance of the user.
Here is an example of how this approach works: clients enter their goals (for example, retiring at age 60 with $300,000.00 in savings), age, income, and current financial assets. The robot-advisor or ML-algorithm spreads investments across asset classes and financial instruments in order to reach the client’s goals.
Companies who provide this option: Betterment, Schwab Intelligent Portfolios
# 3 Turn Algorithmic Trading Into Intelligent Trading
Algorithmic trading is a kind of trading that uses the software for placing trading orders according to predetermined trading criteria like taking into account time, price, trading volume and more. Algorithmic trading allows doing trading without human intervention.
Machine learning technology, however, offers a new and diverse suite of tools to make algorithmic trading more than automatic. In the case of machine learning (ML), algorithms pursue the objective of learning other algorithms, namely rules, to achieve a target based on data, such as minimizing a prediction error. ML algorithms are designed to analyze historical market behavior, determine an optimal market strategy, to make trade predictions more accurate.
Companies who provide this option: Renaissance Technologies, Walnut Algorithms
# 4 Manage Risk and Prevent Fraud
These are the 2 hottest topics for banks at the moment and that is why these projects were addressed first with innovative technologies of analytics, Machine Learning, and Big Data. Banks calculate all possible options for risks and fraudsters and throw them away at the first suspicion.
The main advantage of ML-powered fraud detection systems is that they go beyond following a checklist of risk factors — they actively learn and calibrate to new potential (or real) security threats.
Using machine learning, systems can detect unique activities or behaviors (“anomalies”) and flag them for security teams. The challenge for these systems is to avoid false-positives — situations where “risks” are flagged that were never risks in the first place.
Companies who provide this option: Kount, APEX Analytics
# 5 Prolong Customer Base Interest in Bank Services
In addition to accessing economic activity data, the bank can also receive extraneous data, such as data from social networks or just analysis of online behavior, to add this information to the ecosystem that surrounds the customer. By analyzing this information, which is in Big Data, the bank discovers a large number of new opportunities. For example, if a user discusses the possibility of buying a new car in the comments, the bank can generate loan offers of the same machine that the client dreams of and send these offers to him immediately by email.
# 6 Determine the Best Way for Communication with the Client
Customers require the most comfortable means of communication, which they use by default, such as social media, email or instant messengers. The bank must determine the priority channel of communications and send all alerts, new offers, and contact through them. This will allow the client to feel that the bank is on the same wavelength with him and at the same time will allow the bank to spend less on other means of communication.
# 7 Determine When a Client Plans to Change the Bank
By analyzing the internal and external data about the client, it is possible to determine whether the user is going to leave the bank or not. For example, if a client has not visited physical branches of a bank for a long time, does not visit the website and subscribes to updates from other banks in the social networks, the chance that he or she leaves the bank can be predetermined. At such a moment it is important for the bank to interest the customer by recommending products or offers that the customer is eager for at the moment.
Machine Learning Is a New Finance Hero
It goes without saying, Machine learning is just remarkably good for finance and the promises of this technology including Big Data and Artificial Intelligence is super high. As you can see, there are lots of options, approaches, and applications for improving: choosing an optimal location for a bank, finding the best solutions for a customer, turning algorithmic trading into intelligent trading, managing risk and preventing fraud, prolonging customer interest and many more. I mean, the list just goes on and on and on…