The accessibility and value of consumer data has grown substantially in the past several years. These days, nearly every company bigger than a “mom and pop” shop works to gather and analyze terabytes of data from their customers, hoping to better understand and serve them while one-upping the competition.
In the financial industry, these efforts are particularly intense. Data has the power to shape not only financial decisions (like how and when to invest in stock) but the types of financial products that are available to consumers. So how, exactly, has big data changed the financial industry, and what can we expect moving forward?
Product Diversity and Availability
First, data, has increased the diversity of financial products available to consumers, as well as the accessibility of those products. For example, most lenders have historically offered a wide range of different loan options to consumers; but today, with better access to consumer data, lenders can do a more intelligent risk analysis of each individual customer. Limiting factors like credit scores or debt-to-income ratios can now be mitigated with a greater network of variables, and in some cases, data can help lenders personalize products for individual consumers who need them.
In other words, big data has made it possible for a greater number of people to gain access to the financial products they need—and banks are benefiting because they can offer more products to more customers.
Market Analytics and Profitability
Another breakthrough has been statistical analysis as it relates to the stock market and other investments. Financial institutions have been using variations of algorithmic trading as early as the 1970s, but it’s only within the past decade that AI-powered trading systems have become commonplace. Financial leaders are making use of decades of analytic power to make smarter trading decisions, increasing profitability, and tech startups are using similar algorithms to empower everyday consumers to make smarter investing choices at the same time.
Fraud Detection and User Security
Data isn’t just about making better investment decisions; it’s also about keeping people safer. Leading banks are utilizing the power of big data and machine learning to step up their security game, automatically detecting deviations in consumer purchasing behaviors to prevent and mitigate fraud. For example, if your bank notices a strange series of purchases on your credit card, it can automatically freeze the account and notify you of the threat. Occasionally, this may be inconvenient for customers intentionally deviating from their past pattern of behavior, but more often, this measure can (and will) prevent fraud.
Reduced Manual Procedures
The rise of big data, and with it the rise of machine learning and AI, has also reduced the number of manual processes required in the financial industry. Notorious for its demanding regulatory requirements and ongoing paperwork needs, the financial industry can now lean on algorithms and automated processes to handle work that once required deliberate human attention.
The downside here is that previous human jobs are being displaced; certain manual roles have been fully taken over by a much cheaper, more efficient, less error-prone algorithm. Fortunately, in the financial industry, there’s plenty of room for upward growth; rather than simply being let go, people in these roles are being afforded new opportunities, and are given training on how to utilize (and possibly improve) these new technologies.
Many financial institutions are also using big data to make life easier for their customers. With big data analysis and predictive analytics, banks can predict customer behaviors and provide tools to better suit them; for example, banks may be able to shorten payment delays in some situations. Other customers may benefit from proactive customer assistance when dealing with an issue, or “smarter” customer service platforms.
That said, big data hasn’t been equally beneficial to all financial institutions. Most companies are still struggling with a handful of important challenges:
- Data volume. The most valuable customer data isn’t public or readily available; financial companies need to do the work to gather significant volumes of customer data on their own, one way or another.
- Accuracy and quality. Large quantities of data don’t mean anything if those data aren’t reliable. Establishing processes that gather accurate, reliable data is a major challenge for most financial institutions.
- Security and integrity. Banks are also responsible for storing customer data in a secure, practically fraud-proof way. This is easier said than done.
- Regulations. Banks have to conform to a number of strict regulations about consumer privacy, security, and transparency. These can be incredibly hard to manage in the modern era of big data.
In the coming years, big data is likely to become an even bigger force in the financial industry. Customer data will become even more plentiful, and analytical capabilities will expand further in kind. The possibilities are practically endless.