GIS in the Banking Finance Services and Insurances Sector
The success of Banking Finance Services and Insurances companies depends largely on predicting risks and preventing them at an early stage.
A myriad of challenges lies in the BFSI sector- from entering new markets, agent allocation and branch optimization, early detection and prevention of frauds to streamlining entry and exit strategies.
Unlock the answers to these challenges with the help of GIS-based location analytics.
What We Do
Geospatial data is an asset to the Banking Finance Services and Insurances industry, offering companies advantages otherwise impossible to gain from other data types.
This helps companies to do everything- from tracking competition, sourcing loans to new to credit customers, detecting frauds at an early stage and reducing risks to consolidating business stronghold.
GeoSpoc uses geospatial data that enables companies to realise patterns that are difficult to see using traditional data analytics and visualisation.
Solution Components
Underwriting
A valuable mechanism for ‘New to Credit’ customer selection that complements bureau score through overlay heat maps on delinquency profile, customer behavior and income levels
The solution helps create a more robust credit assessment resulting in a more accurate approval/rejection percentage
Collections
Create, allocate and optimize territories for collection agents for improving efficiency, ensure the maximize collections per executive & help in contact prioritization
Portfolio Management
Enable digital portfolio management through Geospatial tools, data science and AI workflows from a common data repository
Geocode existing addresses for an improved customer experience while ensuring customer integrity
Cross Sell & Up Sell
Create and manage Hyper local offers through geo-CMS platform and deliver the right message to the right person at the right time
Case Studies
Geographic patterns help understand risk profile
Visualize trends and patterns on maps to identify where the high risk or potential customers come from and streamline entry and exit strategies.
Agent Allocation and Branch Optimization
Spatial data of high-risk (negative) areas was only available till pin code level and lacked granularity. This data was primarily acquired from the collections team by their experience of EMI collections in areas, and from industry and government bureaus. However, this data was not collated, verified, refined, or centralized; which reduced efficiency while approving loans.
Spatial data of high-risk (negative) areas was only available till pin code level and lacked granularity. This data was primarily acquired from the collections team by their experience of EMI collections in areas, and from industry and government bureaus. However, this data was not collated, verified, refined, or centralized; which reduced efficiency while approving loans.
