A few months ago I had the opportunity to collaborate with some Data Scientist porting PySpark queries to raw Python. One of the primary areas of concern was aggregation statements. These were seen…
Location Intelligence is rapidly transforming the financial industry. A lot of location data is being constantly generated by financial organizations and they are trying to gain meaningful insights from the data to drive more value-add for the overall ecosystem. There is a huge opportunity for businesses to make more informed decisions using location intelligence.
Banks and financial institutions are leveraging location intelligence to
As a Google Maps Consultant, I have been talking to many organizations in the financial services industry. Most of the forward-looking organizations try to make the overall user experience intuitive and exciting. I was able to identify a few interesting use cases on top of Google Maps which I would love to share with the broader audience.
Advanced Store Locator
(to enhance internal and external customer experience)
In an effort to automate processes and allow users to interact better with their digital platforms, banks have added geolocation features on their websites and mobile apps. This helps customers find nearby ATMs, branches, information like branch timings, product and services offered, book an appointment with the Bank Manager or with Account Managers etc. Customers can also know the fastest route and directions to their nearest branch or ATM. A cool and interactive branch/ATM locator increases your visibility online and can help increase footfall to your location. It also improves overall user experience for your customers while they are interacting with your website . Also, it may bring in more sales by directly guiding clients to your products and services. The main focus is to reduce customer anxiety and boost online customer experience.
For internal purposes, companies use Google Maps APIs to plot POIs on maps, perform data-backed analysis and take informed decisions with location-based context added to the overall data points. For example, before mortgaging a new property, one can check the mortgage value in a particular user location, view existing properties mortgaged by the company on a map and do the necessary calculations based on historic location data. A single View of Property will also help in the entire risk management process.
Address validation Tool
(clean, record and store addresses in a proper format)
Today, one of the major challenges the BFSI sector faces is validation or verification of addresses. For location analytics, organizations do need accurate, clean, and structured databases. Many banks, insurance companies, and financial services organizations struggle with huge amounts of addresses that are entered in a random manner by users, employees or staff. Google Maps Places Autocomplete and Geocoding APIs can come to their rescue.
As a solution, using the auto-complete feature on customer facing platforms, customers start typing their address in the form, they will be provided with the suggestions (in a drop-down menu) and these addresses can be selected, validated and stored in a readymade format for future reference.
Geocoding is the process used to convert raw addresses into spatial data and associates the exact geographical coordinates for the addresses. It helps to pinpoint the accurate location entered by a user. Hence, organizations are able to get a lot of information like where the property/customer is located, assess if an insured property is in a high risk region, does the property require additional risk assessment, can we convert existing stored addresses into structured formats, etc. Google Maps Geocoding API provides clean, reliable and accurate data which can be used for getting meaningful insights.
With the increase in the number of financial frauds and crimes, location intelligence can play a vital role in fraud detection. Using location based data, one can match the purchase location and the current location of the customer. Another interesting use-case, is to match the current purchase location with the previous purchase locations, investment patterns and assign it to a geofenced area. If there are any transactions that do not match the regular purchase behaviour, then the transaction can be marked as potentially fraudulent. This can be flagged to the customer or directly stopped.
Another way of detecting fraud or crime using location intelligence is to obtain insights from the usage of credit cards. Current location of a customer, places where the customer spent most of their amounts, frequency of travel, can all assist in obtaining spending pattern and behavior. Any irregularities in the credit card bill can be looked upon, checked for possible fraud. Many companies are saving large amounts on their support-services by giving customers location-context in their credit card and debit card statements. Customers are able to click on an e-statement and view where they spent the various amounts. This reduces the number of support calls to call centres, saving valuable time, money and resources.
Business intelligence -
(Where should one open a new branch or ATM)
Location intelligence assists financial institutions and banks to analyse a particular geographic area and understand the benefits of one location over the other. This helps banks to decide where to open a new ATM or bank branch, staff and square foot area required.
The use of location data goes beyond simply layering in latitude and longitude. It is all about enriching your data with location.: By layering in relevant and contextual data attached to a particular area, this helps build a fuller picture of the customers there. That, in turn, can help banks understand how to staff the branch and determine realistic sales and profitability targets.
Because location intelligence helps banks better understand the profiles and advantages of one location versus another, it helps clarify where retail locations would best serve their needs.
The use of location-data is paramount. Google Maps Platform is being leveraged by companies worldwide to do more with their data, and redefine customer experience.
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