Here’s How Geospatial Technology can Empower BFSI Sector in India
The Banking, Financial Services, and Insurance (BFSI) sector continue to be one of India’s strongholds for economic well-being. And yet, the sector has been seething with multiple fundamental issues for quite some time.
BFSI companies have not been able to match up to evolving customer needs and technology advancements. Disruptive financial technology (fintech) has been rapidly replacing traditional banking, increasing the pressure on them to innovate and adapt. The COVID-19 pandemic has also put in focus the need for connectivity, predictability, and better user experience, and BFSI institutions have been lagging in this regard.
In order to successfully manage and benefit from digital transformation, banks, financial institutions, and insurance companies today must simultaneously reinvent their conventional approach and integrate these innovative technologies.
In this scenario, location data has emerged as critical support, allowing BFSI companies as well as customers to use the spatial context to make informed decisions. In a cutthroat economy, these correlations also help businesses identify spatial patterns and data, enabling them to provide better service and enhance the overall customer experience.
Applications of Geospatial Technology for BFSI Transformation
Applications of Geospatial Technology for BFSI Transformation
Customer service continues to be the single most important factor behind customer retention in the BFSI sector. Research by Decibel Insights suggests that around 89% of customers are likely to switch service providers due to poor experience. And yet, banking, financial services, and insurance organizations have struggled with the challenges of personalized solutions, resolving customer problems, long waiting times, limited channels, and ultimately low customer retention.
Using geolocation data, banks can easily map out where their customers are and divide their clientele accordingly. They can then determine customers’ preferences and behaviours depending on their regions and territories and offer loans, credit limits, and other banking services in accordance. Field agents can be better structured to handle client interactions on time, based on area-wise demand and concerns.
Product and Location Optimization
With the BFSI sector getting more and more commoditized, there is also increasing competition to stand out and win over customers. The goals are simple – optimizing products and locations to gain maximum customers through higher sales. Interestingly, BFSI institutions have tons of data to support these objectives, but no way to consolidate them.
Geospatial technologies combined with data analytics are a befitting answer. The derived location intelligence can help predict where new branch locations would make the most sense based on customer segmentation and demographic information. Market and networking gaps can be identified and strategically filled.
Agent allocation and their sales strategies can be designed tactically from one area to another based on client preferences, such as which products they prefer, interest rate risks, marketing channels with the best ROI, and services preferred in-bank/in-office services or at-home, and so on. Performance budgeting for banks/financial institutions/insurance companies can be realistically based on actual business possibilities on the ground by looking at previous trends and predictive analytics.
Location-Based Auhentication
Authentication (determining whether someone is really who they say they are) is a keystone for the BFSI sector. This is because both the customer’s and the bank’s money are at stake, no matter how big or small the transaction. The need for authentication is even more pressing in the face of the digital onboarding of BFSI institutions, which leaves them vulnerable to hacking and identity theft.
Location-based authentication, also known as geolocation identification, can be used to confirm an individual’s identity and authenticity just by detecting their presence in a specific area. BFSI companies can set up applications that either send a push notification to a customer’s mobile device authorizing a transaction or triangulate the customer’s location themselves using Geospatial technologies to ascertain whether they are in the same location as the transaction taking place. The technology can also be used in conjunction with mobile banking to prevent fraud.
Agricultural Banking & Insurance
The need to invest in agriculture is ever-increasing due to global population rise, changing dietary preferences for high-value products, and climate risks. Besides, banks and insurance organizations usually have explicit targets for agriculture lending and insurance as part of their overall priority sector targets. This is to protect farmers against moneylenders and risk scenarios by offering them more institutional credit/insurance.
And yet, traditional banking has only increased both farmer and bank woes. The lack of risk allocation in agricultural lending pushed farmers into the vicious cycle of borrowing more but not being able to repay. Crop insurance schemes also failed to compare crop performance based on seasons/years/geographies, and thus did little to reduce volume risks.
This is changing today with the application of Geospatial technologies for generating high-resolution maps from farm to farm, which can then be used to extract aerial visuals and 3D models of the current state of standing crops. Each agricultural loan is geo-tagged and integrated with bank data to update account statuses in real-time.
The anticipated farm yield for the season is calculated using the unit cost and yield ratio standards established for the crop by banks. Images from the current season compared to those from the previous season then show yield variations. If crop degradation or damage is noticed, the expected losses can be readily evaluated for realistic loan provisioning and crop insurance coverage.
NPA Assessment and Follow-Ups
Most financial organizations in India are burdened by high NPAs (non-performing assets) and delinquency and are looking for ways to reduce them. Geospatial data and technologies allow these organizations to look at risk locations in a granular way, thus identifying potentially delinquent customers from the very beginning.
Geographical areas can be defined based on risk propensity using historical data, and this information can be shared across departments. Area-specific recovery initiatives can be launched by superimposing customer data, loan/default figures, and time-stamped ground data on the progress of funded projects and assets. Even larger regions of interest involved in infrastructure projects can be captured using satellite/aircraft imagery or UAVs for this purpose.
Regions, features and objects can also be geo-tagged, digitalized, and monitored at predetermined intervals. Controllers can schedule site visits based on priorities and remotely monitor the branches’ performance metrics at regular intervals online. This results in significant administrative and credit supervision cost savings.
Conclusion
Geographical areas can be defined based on risk propensity using historical data, and this information can be shared across departments. Area-specific recovery initiatives can be launched by superimposing customer data, loan/default figures, and time-stamped ground data on the progress of funded projects and assets. Even larger regions of interest involved in infrastructure projects can be captured using satellite/aircraft imagery or UAVs for this purpose.
Regions, features and objects can also be geo-tagged, digitalized, and monitored at predetermined intervals. Controllers can schedule site visits based on priorities and remotely monitor the branches’ performance metrics at regular intervals online. This results in significant administrative and credit supervision cost savings.
