How Geospatial Data In Fintech Is Expected To Revolutionize The Way You Bank

With an estimated compound annual growth rate (CAGR) of 19.8%, the Global Fintech market will hit $332.5 billion by 2028. And there are no signs of slowing down in sight.

Deloitte says the second wave of fintech is right around the corner. And it involves partnering with technology companies to use data to gain access to new markets, understand their customers, and learn the “secret sauce” that powers innovation.

In a fast-paced industry that requires quick thinking to stay at the top, fintech companies are turning to geospatial data to help improve their offerings.

By using this information, businesses can understand their customers’ behaviors and use that knowledge to create products and services that meet their needs.

In this article, we’ll explore how geospatial data will revolutionize how you bank and participate in any other kind of financial transaction, for that matter.

Let’s dive in. 


What is geospatial data in fintech?

Geospatial data or location data is inherently dependent on location or organized so that it can be easily mapped to various locations.

And it’s becoming more and more common in fintech applications to help financial institutions better understand their customers.

For example, geospatial data helps track where people use mobile banking services or how often they visit their bank in person. Now banks can access powerful data that’s perfect for making data-backed decisions about which locations should receive new branches or ATMs based on customer demand and behavior patterns.

With the growing demand for geospatial data, financial institutions are rapidly investing in data center infrastructure. Fintech companies need the right technology to store and manage new data sources. Without it, they can’t keep up with a competitive industry that wants to serve its customers.

Let’s review some ways geospatial data in fintech is revolutionizing the banking industry. 


Increase location-based services

Location-based services are one of the most popular uses of geospatial data. These services use your phone’s GPS (or other location-based technology) to pinpoint your location and then deliver relevant information based on that location.

Take Starbucks, for example. As you drive or walk closer to a Starbucks brick-and-mortar location, your Starbucks card that’s stored in your Apple wallet suddenly appears on your smartphone’s lock screen. If you weren’t daydreaming of a pumpkin spice latte, you are now.

Banks can learn a thing or two from Starbucks. Starbucks holds more than $1 billion in deposits from gift card sales, both in the physical form and on their mobile app.  

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Not only do they hold more cash than many banks, but they also put geospatial data to excellent use. The simple notification that you are near Starbucks plants a seed in the consumer’s mind. Now, they can’t pass up that pumpkin spice latte on a crisp fall afternoon.

According to a recent study by Capco, customers are highly interested in text alerts and notifications about opportunities to transact more efficiently.

Banks can use Starbucks’ location-based strategy to send a message to your phone when you are near an ATM. This alert is especially helpful if it’s been a while since your last visit, and you might need to restock your wallet with cash.

Or, if you enjoy the buy now, pay later option, you might receive alerts for all the stores that accept this payment method during your next shopping spree.


Improve customer experience with personalized services

The future of financial services is here and more personalized than ever.

In the past, you might have had a relationship with your bank that was mainly transactional: you’d go in, deposit checks, withdraw cash from an ATM, maybe get a loan, or apply for credit. That’s all well and good — but it doesn’t give you the kind of personalized experience that we’re seeing more and more in fintech these days.

Nowadays, banks are looking to improve their customer experience by offering tailored services based on where you live, what kind of lifestyle you lead, and your spending habits.

With this information, they can offer different products or services based on what would be most helpful to each client individually — and ultimately build a foundation of loyal customers.

Consumers want an easy, streamlined experience with their bank. When customers receive offers for products that are contextually relevant, they’re more likely to engage with them. Regarding finding and shopping for financial products today, 65% of banking customers believe institutions should make it easier.

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Geospatial data will become an integral part of customers’ experiences in the future as they interact with their financial institutions through mobile apps.

Fintech companies can use data about people and their assets to make better decisions about product development and deliver personalized marketing strategies.


Enhance fraud detection and prevention

With great power comes great responsibility. Fintech companies have access to a lot of data, and they’re using it in new ways to detect and prevent fraud.

For example, a bank uses geospatial data from its mobile app to determine the location of its customers. This data provides insight into how often customers use the app in certain areas, helping to indicate potential fraudulent activity.

So if someone is logging into their account from Moscow, Russia, instead of Los Angeles, California, for example, that could indicate a red flag — and adjustments are made to freeze the account until the transactions are approved or denied by the account holder.

Other fintech companies are turning to the dynamic duo, geospatial data and artificial intelligence, to create algorithms that detect money laundering based on behavioral patterns found within financial transactions. 

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Geospatial data helps them identify specific areas where this activity occurs (think casinos), allowing them to focus their attention on one area instead of wasting resources elsewhere.

Add in the power of machine learning, and a computer can learn to identify fraudulent schemes from previously identified patterns and then decide whether to approve an ongoing transaction.

Geospatial data has a lot of potential for fintech, but one of the most precise ways it’ll revolutionize the way you bank is by enhancing fraud detection and prevention.

Wrapping up

The wants and needs of consumers are constantly evolving and, frankly, only becoming more demanding. We all want transparency, simplicity, and convenience in every aspect of our lives, including how we manage our money.

Geospatial data will improve banking services and fraud detection by showing how consumers spend their money in real-time. Add in the power of artificial intelligence and machine learning, and you won’t even recognize the banking industry anymore.

It pays to be a mover and a shaker. 

Banks that quickly shift to investing in geographic information systems will set themselves apart from the crowd by creating a competitive advantage that’ll drive long-term business growth.


How Big Data Is Changing The Finance Industry

The finance industry is a highly competitive space. It faces a new generation of disrupting banks and regulations. It’s an industry that needs to utilise big data to drive personalisation, boost customer loyalty, security and fuel everyday investment decisions.

The financial services industry has always been at the forefront of technical innovation. The availability of new datasets has provided a powerful way to understand behaviour and offers new directions for the financial industry to be predictive.

Big data application in financial services goes beyond predicting share prices. New data types are revolutionising the space.

We’re going to break down some examples of how big data is being used in financial services.



Personalisation has long been a priority when dealing with consumers. This is true both inside the finance industry and across other verticals with a strong consumer presence.

Disruptor banks have begun to establish themselves in the finance sector. One thing that these banks have done well is personalisation.

By understanding users spending habits and behaviour, they can offer more personal spending products and recommendations.

For example, in the world of digital banking, if a bank had the right data sets around its customers, it could provide services that truly bring value to the end customer. If a bank understands what their customers spend, where they go and where they work they can, for example, suggest that a travel card could save them a lot of money each month.

It’s in these areas that financial services can learn from disruptor banks. With the right dataset, the banking experience can be personalised for customers. This will allow financial services to boost customer loyalty and drive cross-selling of their products much more effectively.



There are few industries where security and fraud are more of a threat than in the financial services.

As technology has advanced those in the space must be smarter and better adapted to the fast-changing tactics of those looking for weak points in sometimes outdated systems.

Adding location intelligence to the equation adds an extra layer of security for customers and allows financial institutions to instantly provide checks based on where a customer uses its products.

Location offers a new way for security teams to identify fraud. Location data can help to educate security systems on customer behaviour and can form a strong base from which to detect irregularities in financial behaviour.

Financial services continually process an incredible number of transactions. A location overlay which includes operational rules can help to determine when to flag records as fraudulent.

These processes enable financial services to provide better safeguards to their consumers and clients.



The rise of big data carries enormous potential for investors. Many have implemented predictive systems that are designed to understand data sets, digest vast amounts of data and then inform investment decisions.

These data sets have proved successful, but accurate location data is rarely used to optimal effect in this area. Location intelligence provides a more detailed understanding of trends and ingesting these precise datasets can help investors to stay ahead of the competition.

Understanding how populations move and behave en mass is readily available in the online world. Location offers something slightly different. It helps to understand how consumers move in the offline world.

Using location allows minimal lag between an aggregated understanding of consumer trends and investors being able to forecast the performance of portfolios and financial markets.

This allows investors to act quickly and decisively. Location is a fantastic indicator of market, brand or individual behaviour. With the progress made in accuracy by some in the space, this will prove to be one of the next steps in predictive analytics for the financial sector.



For insurance location underlies everything. Many insurers can immediately benefit from quality location data to better model risk and dramatically improve their underwriting and pricing.

In insurance, nearly every data point has a relation to location.

Crime data – understanding crime risk in specific locations including historical data and predictive solutions can provide significant advantages to insurers.

Catastrophe – using data analytics insurers can mitigate the risks involved with catastrophes based on a customer’s location.

Behavioural data – using new kinds of behavioural data, such as location, can allow insurers to understand consumer behaviour better. This helps to predict risk and assist with pricing.

These datasets are now available in large quantities. Precise location allows for more accurate insights and the ability to generate this data, as well as store and process it, has changed the game for the insurance industry.


New revenue

For financial businesses, consumer data also holds the key to new revenue. It allows companies to maximise revenue from existing channels.

For example, using precision data marketing financial companies can identify products and services that are a much closer match for specific customers. This provides more value for both customers and branded partners.

Understanding customers behaviour allows companies to understand which type of person is more valuable for their business.

Using this data, it’s possible to build lookalikes and use this to drive more targeted marketing activity to customers that represent a greater potential for higher revenue.

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