Most of us are at least somewhat familiar with what constitutes a competitive edge in businesses, even if our ideas only scrape the surface. A sprinkle of innovation here, some enticing offers there, maybe some top-notch output speeds — all valid angles that play their integral parts in thriving organizations, but perhaps the actual winning element lies somewhere in between.
Besides, it all really depends on which field you look at. For industries rooted in financial technology, we can confidently say that one particular type of innovative software takes the leading position, and it does not look like that will change any time soon. We are, of course, referring to the elephant in the tech room: artificial intelligence.
The Full-Scale Game-Changer
At this point, it’s safe to say that the evolution of the fintech industry is intrinsically linked with that of AI. Financial technology’s roots are based on automation, so “robo-advisers” and algorithms are only the next natural step. While not fully integrated, both AI and machine learning are being pushed into taking on more prominent roles as we speak.
Every branch has its ambitions regarding AI and its growth, but this also poses a pretty big question: Can (and should) fintech companies dive head first into bringing AI into their established projects? While the short answer is between “well…” and “maybe,” the long answer is what we’ll elaborate on from this point.
Evaluating Your Current System
To answer the burning question above, it’s only logical that you begin with the evaluation phase. Before one dives into the actual integration process, it’s essential to consult expert members or external sources regarding the company’s existing infrastructure, forecasted capabilities, and data sources.
After all, while AI models are becoming more readily available by the minute, sensitive sectors like finance require more than their fair share of considerations and legal compliances. Moreover, the presence of AI does not mean you won’t be relying on human experts — quite the opposite, in fact. Most team leaders will want to reinforce their existing development branches with additional Python developers, as it’s currently the most widely applied high-level language for deep learning software.
If you believe your organization is lacking in certain areas, consider looking at online AI readiness quotients and indices, which evaluate whether or not you can leverage AI based on critical areas such as data infrastructure and capability-enablement.
Selecting the Right Set of Tools
For cost-saving and time-optimization purposes, developers can (and will) rely on established frameworks and libraries — it’s a necessity, if anything. However, as these collections consist of pre-built assets, the extent of their future efficiency is subject to change. Regardless, our focus is on the “now,” which is why we’ll look at some of the most widely applied options on the current market and their respective offerings.
TensorFlow
Google’s framework TensorFlow provides a fairly malleable set of building blocks for AI integration and further training. Thanks to its extensive API support, deployment is a far smoother experience than you might expect. In the context of fintech, TensorFlow can be used for tasks such as fraud detection, credit risk modeling, and algorithmic trading (unsurprisingly, the most popular choice).
PyTorch
Developed by Meta’s own AI Research lab and now part of the Linux Foundation, PyTorch has a particular focus on dynamic graphs and Pythonic syntax. The latter makes it an intuitive and developer-friendly choice, and its application in fintech mainly revolves around portfolio optimization and time series forecasting.
Scikit-Learn
A rich Python library that provides sufficient ML and data analysis tools. Simple and flexible (compared to its rivals), Scikit can aid in the deployment of fraud detection AI with the help of predictive models that analyze patterns and anomalies.
H2O.ai
Suitable for programmers and non-programmers alike, H2O primarily targets automation in machine learning and features numerous built-in algorithms that simplify hyperparameter tuning — in other words, the learning rate. The graphical interface makes it an alluring option for analyzing historical data and behavioral patterns, which in turn help determine creditworthiness and assist in lending decisions.
Early Training and Testing
Needless to say, the selection process is only the beginning. You and your team of experts must also ensure these tools can effectively address specific needs, such as fraud detection, dataset-assisted forecasting, automated trading, or even “primitive” tasks such as document processing. This can be achieved through meticulous training and testing – both critical, as they help validate AI models’ end-user reliability, real-time accuracy, and scalability before deployment.
Training Phase: Fintech companies are expected to invest significant resources in training their AI through external datasets. Naturally, the data must be audited, relevant to the use-case scenario, and comprehensive enough to provide “fallback” options for the AI. It’s essential that during this phase, models are exposed to diverse sources to minimize biases in forecasting and improve overall generalization.
Testing Phase: Technically, while the training phase is infinitely ongoing, you’ll still have to provide rigorous testing once you’ve reached a sufficient applicability level for your AI. This phase entails stress-testing the models against outlier data – variables, errors, and the ‘funny’ things in between. Evaluating and improving these AI performance metrics through different stress conditions ensures they can handle real-world applications without faltering.
Collaboration and Continuous Learning
As AI changes and scales appropriately, maintaining an adaptive learning process takes top priority. Newer, refined learning mechanisms continue to pop up, allowing AI to adapt to changing financial landscapes and automatically improve without being retrained from scratch. However, this is easier said than done alone, which is why fostering collaborative work is a must.
Collaborating with tech veterans can open more than a few doors for you and provide insights into AI deployment and future-proofing. Beyond surface-level benefits, such partnerships may also facilitate shared learning and improvements in democratized AI applications, leading to more robust, self-sustaining solutions.
Furthermore, by implementing feedback loops within AI systems, the processes of identifying and fine-tuning become much more streamlined. Human input is invaluable in this case, as it involves gathering feedback from system outputs and using them to make informed adjustments. The entire process enhances the model’s performance and ensures your services remain relevant.
Our Round-Up
While fintech-centric AI applications are complex and require meticulous planning, it’s nothing that time and a proactive spirit can’t tackle. The benefits of enhanced efficiency, improved customer/end-user service, and innovative financial solutions make this endeavor more than “slightly” worthwhile. By addressing these critical areas—evaluation, tool selection, training, and collaboration—your company can slowly but surely position itself at the forefront of AI-powered financial solutions.