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The proliferation of Big Data and the use of artificial intelligence has changed the game for financial services. Now more than ever, it’s critical that companies have trustworthy ways of performing their practices, finding new investments and conducting research. And all of those boxes can be checked off in one fell swoop when companies begin using data analytic tools.
In this blog, we will outline why data analytics in finance is important and how to incorporate big data into your workflow. We'll also discuss why the right data is crucial for finance data analytics.
Customer satisfaction is at the forefront of everyone’s minds in this current financial climate. Consumers are extra wary of making new investments or entrusting their assets to both familiar and unfamiliar third parties. To combat this, research tools use automation and AI to secure and report on customer data analytics.
Rajat Jhingan from Akounto Inc. points to Dickey's Barbecue Pit as a great case study for how big data can help improve the customer experience; in a recent report, Dickey’s CEO said that the trial of using big data in a smaller setting is so successful that the chain plans to roll out the process across 350 restaurants. Dickey’s stakeholders use data analytics to oversee restaurant performance, customer concerns and to project sales numbers.
Ultimately, Dickey’s use of big data has helped them better understand consumer needs. CIO Laura Dickey said: “Its biggest end user benefit is bringing together all of our different data sets from all of our source data – whether it’s our POS system in stores directly capturing sales as they happen, or a completely different source such as a customer response program, where folks are giving us feedback online or in different survey formats.”
Another massive benefit that big data tools have to offer is with predictive analysis. Using a non-biased equation to spot trends in numbers is a fool-proof way to estimate future earnings and losses. As Jhingan puts it, “Data analysis helps crunch big numbers enabling real-time market insights, risk assessments, and other crucial metrics.”
In one case study, Uber used big data to analyze the demand of cars in certain areas. They implemented machine-learning algorithms to pick up on when demand was on the rise so they could alert more drivers and, ultimately, induce surge pricing to benefit from higher demand.
This can be applied to the finance industry for even bigger company gains. Jhingan points out the example of BlackRock’s Aladdin platform, which “integrates information from various sources, including market data, economic indicators, and news, to provide a comprehensive view of market conditions and help inform investment strategies.” This in turn allows BlackRock to confidently suggest new investment opportunities and hold a critical eye at potential areas to be wary of.
MORE: Analyze market, media, and trends data for better business intelligence
Advanced analytics and deep dives into data can allow for companies to build a better understanding of the long-term, big-picture view of their own finances and the finances of the investment opportunities they trust. This happens not just with the aforementioned fraud detection abilities, but also by analyzing credit data and detecting downturns.
As Jhingan points out, JPMorgan Chase have used big data to successfully mitigate risk. “The Corporate & Investment Bank (CIB)'s solutions include predictions, pricing models, client intelligence, virtual assistants, news analytics, and anomaly detection. These techniques have allowed JPMorgan Chase to reduce its risk exposure and enhance the stability of its operations.”
MORE: What the updated Wolfsberg Principles mean for financial services
When it comes to running the numbers side of a business, compliance is a huge concern. Businesses want to ensure that their data is coming up clean and that there are no issues like criminal activity, money laundering or fraud happening under their radar. By using vetted finance data APIs and automating the process of scanning company and financial data , stakeholders can rest assured that any and all red flags will not slip through the cracks.
In a blog about the benefits of big data, CEO Views writes that “If you are in the process of obtaining compliance certifications, you must maintain the risk associated with sharing the data with vendors appropriately. Big data analytics can help you manage vendor-related risks.”
Financial data is helpful for specific company reports, but it also applies in a more macrocosmic setting. Beyond simply tracking businesses and monitoring consumer data, big data tools will help financial providers to see the market as a whole and gain insight into larger global and national trends.
Walmart makes great use of this ability by bringing larger trend reporting, such as weather forecasts, into its business strategy. The company boasts of improving consumer experience by optimizing the checkout process, identifying and avoiding supply chain blockages and even predicting a customer’s future purchases based on their areas of interest.
MORE: Eight ways to use alternative data to improve your business modeling
As the world transitions into a more tech and AI-forward culture, businesses need to find ways to use technology to their advantage before they fall behind. Big data tools provide many helpful features that can result in improved customer experiences, risk mitigation, better investment recommendations and increased security across the board.
Nexis offers a trustworthy tool where companies can dive into tons of data from thoroughly vetted sources, all in one easily-searchable place to make your financial data analytics streamlined and more efficient. Contact us today to schedule your demo.
Data analytics plays a crucial role in financial services by enabling institutions to gain valuable insights from large datasets. It helps with risk management, customer segmentation, investment analysis, regulatory compliance, and optimizing operations through predictive modeling and data mining techniques.
A data analyst in financial services is responsible for collecting, processing, and analyzing complex financial data to uncover trends, patterns, and insights. They use statistical and quantitative methods to inform decision-making processes, identify risks and opportunities, optimize portfolios, and drive strategic business decisions within the organization.