When’s the last time you wrote a check or visited your local bank? Sure, the old checkbook comes in handy from time to time, but your debit card and mobile banking apps likely get much more action nowadays. This digital shift was already underway long before COVID-19 accelerated the use of contact-free payments, deposits, and transfers. As a result, the use of artificial intelligence (AI) in banking—and data science in finance, generally—also has become increasingly important.
While consumers appreciate the speed and convenience of online banking and payments, financial institutions—which must convert paper-based records to digital anyway—can reduce the time and cost of processing transactions when it’s done digitally. More digital banking by customers also means less of a need for physical branch locations to service in-person transactions, contributing to additional cost savings.
The growing role of AI and data science in finance, banking, and insurance has created enormous demand (and opportunity) for professionals looking for a solid career path. We’ll take a closer look at this trend, which shows no signs of slowing down, and what you can do to pursue a finance-focused career in data science or AI.
AI and Data Science in Finance: Key Innovations
The white-hot financial technology (or “fintech”) sector is being driven by some of today’s most cutting edge technologies, as both consumers and financial institutions seek reliable and secure transactions with minimal need for customer service interactions. For instance, AI chatbots have evolved to the point where they can handle a substantial portion of interactions that once required humans.
Financial institutions have embraced these five technological innovations in particular:
- Open banking
- Mobile banking
- AI and machine learning
- Microservice architecture
When you authorize credit card or cash payments through PayPal or make a purchase using Square, you’re relying on application programming interfaces (APIs) to share data. These are just a couple of the more common examples of open banking, the concept of using APIs to let third parties process customers’ financial data (with proper consent, of course). Another example is the access of financial data by the ubiquitous credit scoring firm, FICO.
The importance of data science in finance cannot be overstated when discussing the open banking trend. Data science determines how these data exchanges are structured, the standardization of the data’s meaning, and how the data can be analyzed. Meanwhile, AI and machine learning have improved and automated the way third parties draw inferences from the data and thus how financial institutions make data-driven decisions.
Blockchain provides a highly secure ledger of transactions that allow all parties to validate those transactions. As opposed to the “lock box” approach to financial security, blockchain gets its power from its transparency. If someone wanted to manipulate or corrupt the data, the transparency of the shared ledger would expose such an attempt. This complex technology is only beginning to become familiar to the public beyond the popular, blockchain-derived cryptocurrency Bitcoin.
Over time, blockchain promises to find uses in a broader range of financial transactions, allowing more types of transactions and documentation to be executed securely in the digital domain.
Mobile banking is now commonplace, offered by nearly all major banks. In addition to accessing your bank records in real time, these services enable you to deposit checks using your phone’s camera, pay bills within the app, set up new accounts, and perform other tasks that previously required a visit to the bank or the sending of paperwork. Again, this trend benefits both the consumer and the financial institution by reducing time and expense, increasing speed, and expanding the types of services they’re able to offer.
What’s relatively new in mobile banking is the emergence of “neobanks”—banks that only operate online—which have the potential to offer lower fees and greater efficiencies for many banking services. To the extent that they can provide those services with more convenience and less cost than traditional banks, you can expect the traditional banks to restructure their mobile banking operations so that they look more like neobanks with assorted traditional services available.
AI and Machine Learning
AI and machine learning power many banking, financial services, and insurance (or “BFSI”) applications. AI (and data science) in finance drives trading systems and pricing models. AI in banking, for consumers, drives services such as credit management. The AI field of natural language processing (NLP) allows automated customer service through voice-response systems and chatbots. While early iterations of these automated systems left a lot to be desired (“May I speak with a human, please?”), they have come a long way.
These and many other BFSI applications will need continuous improvement, so that customer service keeps making substantial improvements and transactions can be processed faster, more efficiently, and with fewer errors. All of this means AI in banking is here to stay.
Microservice architecture is a new way of approaching the structure of information systems. Traditional software created large, interdependent systems of software functions, which led to systems that were difficult to update or modify. In other words, changing one part of the system would usually have unintended impacts on many other parts. This slowed down the deployment of new services or upgrades, since it wasn’t conducive to constant, incremental changes.
A solution to the old way of doing things, microservice architecture breaks down complex software systems into smaller independent components, each providing a defined service. Components can be modified independently, so long as they continue to provide the same services as before in addition to any new ones. This allows organizations to make targeted changes without creating chaos across the system.
Using data science and AI in banking and finance microservice architecture helps ensure efficient and error-free software development and deployment. Specifically, this includes DevOps and continuous integration and continuous delivery (CI/CD).
Learn the Skills Needed to Apply AI and Data Science in Finance
As financial institutions move transactions and even entire operations onto digital platforms, concerns about privacy and data security have increased. Artificial intelligence also is being used to address security concerns, particularly the emerging use of AIOps. While digital security technology continues to become more sophisticated, there is still a growing demand for more cyber security professionals.
If you want to get involved in the exciting new opportunities created by the increased use of data science in finance, AI in banking, and other such developments in the digitization of financial services, now’s the time. And while social distancing guidelines implemented in the wake of the COVID-19 pandemic will eventually subside, the digital banking trend will continue.
If you need to get your team up to speed on AI and data science, Simplilearn offers comprehensive corporate training programs like our AI and Machine Learning Courses or PG in Data Science. Both of these programs feature Simplilearn’s proven applied learning system that combines a live, online classroom setting with self-guided video instructions and hands-on, industry-aligned projects.
As the world of technology continues to transform our lives, it’s time to seize the opportunity. The future awaits!