Finance services are the leading sector of machine learning (ML) adoption. There are many reasons why. First, there's so much to gain in many activities related to finance. Second, the infrastructure of banks, insurance companies, and FinTech startups suit the process best.
ML is disrupting the financial industry in an unprecedented way. It is broadly used in cyber-security, trading, customer service, portfolio management, or legal and compliance areas.
Why is Machine Learning such an excellent fit for the financial sector? Effective ML systems need big data to give out precise predictions. The financial industry is all about collecting hard data. The information about the transactions, their context, localization, time, and frequency can be cross-matched. This kind of experiment, when performed with the right management system, can lead to building software solutions that create a competitive advantage.
Financial institutions have a tradition of gathering large sets of historical data. At times this practice was required by the regulators. This makes a great founding for automation.
Software developers are working with ML for the financial sector, and the FinTech companies most often use solutions based on Python language, which makes software development teams specialized in Python even more in demand.
Cyber-security is a substantial part of FinTech as criminals target the sector where the money is. It's a different story to phish access to your social network site and your banking account.
Preventing data loss, flagging suspicious behavior are some of the tasks that can be performed by machine learning algorithms.
Cyber-security FinTech companies are growing fast as the sector embraced startup lean methodology as the most effective model for innovation.
The main threat vectors that the financial companies need to face are related to privacy, legacy software, compatibility, third-party security, money laundering, digital and the use of data by third parties, and cloud environment.
AI can be either preventive or proactive in mitigating the risk of fraud. You can monitor customer behavior and flag suspicious actions.
ML methods can react in real-time to transactions. The neural networks can be trained to detect activities that can be fraudulent. The next step is to contact the client directly via phone or email to ask if it was her/him that made the transaction or logged in. This is the best way to perform access control.
Finance sector companies use ML techniques for fraud detection. Frauds are a great challenge in the industry, and there's a great incentive to invest in scalable automatic solutions, which makes it a great field for AI development.
ML algorithms can be trained on big historic data sets. Being pointed what was fraudulent activity and what was not one in the past, and later instantly flag suspicious activity.
This is a problem better known for the financial industry insiders. False positives are situations where a legitimate transaction is automatically declined as a suspicious one. These false declines are a result of imperfect fraud detection automation. There are several companies offering this "second layer" of services, such as Aida or Socure.
Automated advisor mobile apps that manage clients portfolio or help run a household budget based on financial data is another interesting trend. Just like your email can be sorted by a simple ML algorithm (even a simple decision tree), the same can be done with your spendings.
ML solutions can also manage some more complex tasks, such as credit card consolidation, mortgage refinancing, or investment management.
Automation has a long tradition in trading. It's all about fast execution, and machines have long been much faster to make simple decisions based on the new data. In algorithmic trading, computers execute algorithms to place a trade.
High-frequency trading has some two-decade-long tradition. As you can imagine, there are massive data sets to analyze and train the algorithms on.
Automatic trading allows us to act faster and can cross-match information from many different markets.
There are also some experiments involving autonomous hedge funds managed by AI. Although this is an extremely complex field, and a lot has to be done to introduce some successful ML-based solutions, the motivation is strong.
Social scoring is a thing of the future that may easily turn dystopian. However, nothing can stop financial companies from using big data on customer behavior for profiling. FinTech startups are offering quick loans that are granted on base of instant automatic credit scoring analysis.
This is a core business of any financial institution - be it an insurance company or investment bank. The trick is to read the future risks correctly. Over the centuries this has always been the key to making big money.
AI algorithms can be trained to make accurate forecasts for individual customers, companies and whole markets. Risk management products are one of the most complicated ML products.
Real-time information discovery system Dataminr and AI-powered search engine for market intelligence AlphaSense are one of the leaders in this segment.
Chatbots are popular solutions in many sectors, and finance is not an exception. With the development of natural language processing frameworks, it's getting easier to automate the earliest stages of sales and customer service. On the other hand, it's getting more and more difficult for the customers to distinguish a human consultant from a bot.
Chatbots can also easily detect the sentiment of a client. Emotions such as frustration can be flagged and a skilled consultant can intervene fast.
Legal tech is a fast-growing field of FinTech. Banks and insurance companies spend billions on regulatory issues. The legal aspect of their business bears often some hidden risks and opportunities.
It may sound strange, but still, about 30% of settlements (which is transferring securities to the buyer's account and cash to the seller's account) is performed manually. However, ML algorithms are pushing this number down, as the neural networks get more and more skilled in pointing out why a transaction failed, and often suggest a solution.
Trade settlement is the process of transferring securities into the account of a buyer and cash into the seller’s account following trading stocks.
Despite the vast majority of trades being settled automatically and with little or no interaction by human beings, some 30% of trades fall through and need to be resolved manually.
The use of machine learning cannot only identify the reason for the failed trades, and it can analyze why the trades were rejected, provide a solution, and also predict which trades may fail in the future.
Developers for years have loved Python because you could build practically andy software with it very fast. However, until recently it was perceived as a technology not serious enough to be suitable for financial software projects. The industry used to choose Java over Python.
This has changed. At the moment, Python is on its way to becoming the most popular programming language. It proves to be a great and efficient solution for quantitative analysis and other tasks typical for the financial sector.
At the same time, most of the machine learning frameworks, platforms, and other tools are written in Python. The list includes Google's TensorFlow, Keras, Theano, PyTorch, and many others.
That's why if you are building a financial product or thinking of including some ML methods to your process, it's good to get in touch with a Python development team, that has experience with bespoke projects.