Machine Learning Trends in FinTech and Why Python is the Key

Cybersecurity is a substantial part of FinTech, as criminals target the sector where the money is. Phishing access to your social network account is a different story from phishing access to your bank account.

Preventing data loss and flagging suspicious behavior are some of the tasks that machine learning algorithms can perform.

Cybersecurity FinTech companies are growing fast as the sector has embraced the lean startup 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 identity, the use of data by third parties, and cloud environments.

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 to transactions in real time. Neural networks can be trained to detect potentially fraudulent activity. The next step is to contact the client directly by phone or email to ask whether they made the transaction or logged in. This is the best way to perform access control.

Finance sector companies use ML techniques for fraud detection. Fraud is a great challenge in the industry, and there's a strong incentive to invest in scalable automatic solutions, which makes it a good field for AI development.

ML algorithms can be trained on big historical data sets. Once shown which past activity was fraudulent and which was not, they can later instantly flag suspicious activity.

This problem is better known to financial industry insiders. False positives are situations where a legitimate transaction is automatically declined as suspicious. These false declines result from imperfect fraud detection automation. Several companies, such as Aida or Socure, offer this "second layer" of services.

Automated advisor mobile apps that manage clients' portfolios or help run a household budget based on financial data are another interesting trend. Just as your email can be sorted by a simple ML algorithm (even a simple decision tree), the same can be done with your spending.

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 at making simple decisions based on new data. In algorithmic trading, computers execute algorithms to place a trade.

High-frequency trading has a roughly two-decade-long tradition. As you can imagine, there are massive data sets to analyze and train the algorithms on.

Automatic trading lets us act faster and cross-match information from many different markets.

There are also experiments with autonomous hedge funds managed by AI. This is an extremely complex field, and a lot has to be done before successful ML-based solutions appear, but 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 offer quick loans granted on the basis of instant automatic credit scoring.

This is the core business of any financial institution, be it an insurance company or an investment bank. The trick is to read 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 among the most complicated ML products.

The real-time information discovery system Dataminr and the AI-powered search engine for market intelligence AlphaSense are among the leaders in this segment.

Chatbots are popular in many sectors, and finance is no 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 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 side of their business often carries hidden risks and opportunities.

It may sound strange, but about 30% of settlements (transferring securities to the buyer's account and cash to the seller's account) are still performed manually. ML algorithms are pushing this number down, as neural networks get better at pointing out why a transaction failed, and they often suggest a solution.

Machine learning can not only identify the reason for failed trades but also analyze why the trades were rejected, provide a solution, and predict which trades may fail in the future.

Developers have loved Python for years because you could build practically any software with it very fast. However, until recently it was perceived as a technology not serious enough for financial software projects. The industry used to choose Java over Python.

This has changed. Python has since become one of the most popular programming languages, and it has proven to be an efficient solution for quantitative analysis and other tasks typical of 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 adding ML methods to your process, it's good to get in touch with a Python development team that has experience with bespoke projects.