Data science has improved financial services by speeding up processes that would have usually taken a long time. For example, SafeGraph helped one of their financial services clients by providing them with data to assess whether or not customers would walk into a bank during the COVID-19 pandemic. This helped the client make an accurate assessment of how the pandemic would affect that particular bank, and aided the bank in making the right business decisions moving forward.
In this article, we’ll explore other ways big data can be used in financial services. We’ll also suggest some ways you might be able to make use of it, depending on your niche or your needs. We’ll cover:
It’s important to look at the 7 top big data use cases in financial services to understand the specific impacts of these technological changes on the banking world. And aside from these big data use cases, the financial sector has reaped other rewards from advancements in data science that may not be immediately obvious.
It’s important to note the most significant benefits that data science has brought to the financial industry as a whole. These small changes have made huge differences to people’s livelihoods, including the ways they conduct work.
While we’ve listed 4 huge benefits in this section, you can check out SafeGraph’s Financial Industry page to find out more.
The financial services industry is constantly evolving, so data science use cases for financial firms are, too. For example, risk assessment and management is something that is incredibly important in the financial industry. Banks must be very careful about whom they lend to or invest in. As such, the ability to assess and manage risk faster and more efficiently with data makes the lives of bankers much easier.
Insurance risk, whether related to a property or a person, is largely dependent on how people interact with a particular space. Data science models can shed light on how consumers move throughout a community, including which businesses they go to and when they go. This can inform general liability risk, as a location that gets more visitors has a higher risk of someone getting hurt there. SafeGraph explains general liability using geospatial data, exhibiting that insurance risk can be more accurately calculated using alternative data.
Insurance risk is vital information for insurance companies to be able to determine whom they will and will not accept as clients.
Data science has made it possible to analyze data in real time, as opposed to waiting for data to be processed and made available. This means financial services firms can respond to trends quickly and make decisions that push them ahead of the competition. Geofencing, geotargeting, and beaconing are all examples of real-time analytics.
Real-time analytics are especially useful for understanding and responding to consumer behavior. In an ultra-competitive market, financial firms and banks need to know what their consumers want and need, and when they want and need it. Real-time analytics makes it possible to uncover this crucial information. This lets businesses develop targeted marketing campaigns to meet consumer demand and win market share.
Businesses are beginning to move their decision-making processes away from individuals and towards machine learning algorithms. These reduce human error while also increasing efficiency. Leveraging models that predict risk associated with particular investments or ventures enables financial services firms to make decisions quickly. At the same time, they don’t need to sacrifice the quality of their due diligence and investment research.
At a more consumer-facing level, financial planners assess whether or not a person is in a position to buy a mortgage based on their lending and credit history. Before machine learning, this was a very manual and individual research process. With sophisticated pre-built data models, approvals are much quicker and more reliable.
Financial institutions are using machine learning tools to identify unusual consumer spending patterns or behaviors in real-time. This helps banks act quickly and effectively to reduce losses for both businesses and consumers. For instance, in response to the rise of cybercrime, many banks have algorithms in place to prevent further spending if they detect a credit card displaying unusual activity.
In order to effectively understand and reach customers, it is important to segment them into categories based on their likes, dislikes, needs, socio-economic status, etc. Financial services firms can then develop products and services designed especially for each segment. For a parallel in a retail environment, a business might split their clientele into higher and lower gross income segments. These would depend on the customers’ demographics and how much disposable income they are expected to have. The more disposable income they have, the more they are expected to spend in the store.
Data science allows for the instant analysis of many different data sets from the past and present. This makes it easier to predict the direction(s) in which the market will go, and which investments will be more or less feasible based on those trends. This simplifies decision-making for financial institutions.
For example, an investment company is likely to use statistics to decide which stocks to invest in over a long period of time. They can then use their expected investment profit to offer products to their clients and set their rates.
Financial market analysis results are used by financial firms to choose whether or not to invest in a stock, company, or commodity. Data science can automate and expedite this process, while also producing more reliable scientific results. For example, algorithmic trading can be used to choose which stocks to invest in. This is where advanced mathematical formulas guide bankers in choosing the best stocks to invest in, as well as the best long-term strategy for managing these investments.
The financial services industry has made significant strides in providing innovative solutions for predictive analytics, risk modeling, and customer engagement – all thanks to data science.