Learn more about the impact of Fintech AI in our modern world today. Be surprised it affects your financial aspect.
These notoriously cumbersome tasks have now become fully automated, adaptive, and nearly infallible. AI-driven processes in FinTech AI have become extremely fast and powerful in recent years--and they will continue to do so.
Long gone are the days of physical banking. FinTech AI has ushered in a new age. People now handle money based on technology and less on traditionally physical appointments to the bank. As such, AI has brought in new ways to improve these processes for a seamless and secure user experience.
AI First has many different use cases across different industries. For instance, AI First enables projects that improve an existing process. In the realm of FinTech, AI is continually improving fraud detection systems. This is especially important in an industry where fraud can wreak havoc and rake in losses in the order of millions.
It has become an industry standard. Most financial institutions would agree that having AI improve fraud detection systems is the best way to shave costs and mitigate potential losses.
Another way AI First improves existing processes in FinTech is through risk management. Risk management in Finance refers to the evaluation and minimization of financial risk. This is an essential process because brokers and investment managers are often in charge of protecting their client’s money in an increasingly volatile world economy.
Third, AI First gives way to projects that can augment manual processes. For example, in FinTech it is common for investors to attempt to project potential gains and losses despite the inherent volatility of the stock market. Now, AI can augment this process by doing the projections for the investors and then giving them the necessary information to make the right decision. In this case, AI simply augments but does not automate because at the end of the day, it is the end user making the final decision. AI is there simply to offer a data-backed analysis.
In Finance, ML plays an important role in automating business processes, securing against fraudulent transactions, aiding brokers in portfolio risk management, and many more. There is a lot of data to be had especially since daily transactions are at the scale of millions. Extracting insights are especially important because in Finance, mistakes are quite literally extremely costly. By relying on the near infallibility of well built ML systems, businesses save billions of dollars by avoiding mistakes.
A major FinTech company has approximately 33.8 million transactions per day and $22,577 payments per second. Imagine if their systems were to bug out for five minutes. There is a potential loss of $6,773,100.
Architecture-wise, this company has one of the most complex hybrid-cloud environments in the industry. Paypal uses a combination of both relational and non-relational databases. Additionally, they have over 238 Petabytes in data storage. Again, that is a massive amount of data to work with!
Currently, one of the primary uses for AI in this company is for fraud detection. Banks all over the world lose exorbitant amounts of money due to fraud. In particular, the company switched over from using the logistic regression model to gradient boosted trees (GBT). From there, they were able to detect fraud in real-time 10-20% more accurately than traditional models.
In this particular case, we see how it is important to be in charge of the model used. This will allow the company’s services to evolve as the circumstances evolve. For an AI First company like this one, they leverage on every component in the AI Tech Stack to achieve this level of competence.
A company has developed a crowd-sourced hedge fund model based on a collection of financial models and individual predictions. This is especially relevant in FinTech because portfolio management in an increasingly unpredictable market has also exponentiated in terms of difficulty. Human analysis is still prone to error regardless of how experienced the person behind the analysis might be.
This company applies machine learning to predict the stock market. In fact, they also open source their own tools so as to equip their users with the power to create their own models. The beauty in making their technology open source is that they are able to accept predictions from these users in order to control the capital of the company's hedge fund. They reward users with the best predictions. Hence, they incentivize crowd participation in bettering their models and predictions.
They have effectively gathered the best minds of the planet to join forces and build the hedge fund’s brain. They describe their tournament as the “Hardest Data Science Tournament” on the planet. This encourages programmers with a knack for interesting and challenging problems to contribute their knowledge to the hedge fund in exchange for Bitcoins.
This model works because in terms of solving hard prediction problems such as stock market analysis, a huge amount of brain power is needed to design a model that will tackle the problem with efficiency and accuracy. So how their model is built--is by taking the best performing models from its pool of submitted models and using that information to improve their own model.
The effort is meant to result in a smart hedge-fund model that will disrupt how market analysis is done. Once this model pans out, brokerage companies may leverage on this model to quickly generate predictions that are data-driven and influenced by petabytes upon petabytes of historical market data.
Here at codvo.ai, our business and technology experts are passionate about promoting FinTech AI. In a world where digital currency is increasingly becoming a need for the common man, we believe that using AI to drive sensitive processes has also become a need. These days, security, speed, and accuracy are imperative in any FinTech process. Any lapse in judgment can quite literally cause millions or billions of dollars in losses. Hence, our utmost care in fusing AI with FinTech.
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