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GIS for Microfinance: Mapping Reach, Risk and Field Productivity

Microfinance in India is lending done on foot. Loan officers meet joint liability groups in villages, collect instalments on fixed days and judge households they can see but rarely map. That makes location the hidden variable behind growth, delinquency and cost. Where a branch sits decides which centres it can serve in a day. Which districts carry too many lenders decides future stress. Which villages flood decides next season's arrears. GIS turns these questions into maps and dashboards that credit, operations and risk teams share, so expansion, collections and portfolio limits are set with evidence rather than habit.

Geo-Business IntelligenceMonitoring SystemsLive TrackingField ValidationRemote SensingData ScienceMachine Learning
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What the evidence shows

21 to 10 days[5]

Loan turnaround at an Indian MFI after a tablet field app (India)

In an Accion case study of Ujjivan in India, 68% of loans were completed within 10 days, down from 21 days, after officers moved to a tablet-based field application. This is published industry evidence, not a GLOBEIR result.

144 to 337[5]

Caseload per loan officer at an Indian MFI (India)

In the same Accion case study from India, caseload per loan officer rose from 144 to 337. Ujjivan attributed most of the gain to the digital field app and the rest to training and new products.

40 minutes a day[5]

Office data-entry time saved per loan officer (India)

Accion found Ujjivan's loan officers in India saved an average of 40 minutes a day of office data entry once field data was captured digitally.

Nearly 68%[3]

Industry portfolio held in the top 200 districts (India, March 2026)

Sa-Dhan's March 2026 quarterly report shows the top 200 districts held nearly 68% of the portfolio and the top five states 57%, which is why district-level concentration monitoring matters for every MFI.

A large, rural and geographically concentrated market

At 30 June 2026, MFIN reported microfinance operations in 36 states and union territories and 718 districts, with a portfolio of Rs 3,28,708 crore. The book grew 1.1% in the quarter, a second quarter of growth after seven quarters of slowdown, but was still 6.9% lower than a year earlier. NBFC-MFIs held 44.3% of the market and banks 25.3%. PAR 31-180 fell to 1.6% from 5.6% a year before.

Sa-Dhan's quarterly report for March 2026 shows how spatial the business is. Lending reached 775 districts, yet the top five states held 57% of the portfolio and the top 200 districts nearly 68%. Fourteen districts each carried more than Rs 2,000 crore. The reporting lenders ran 26,077 branches with 1.25 lakh field staff, each serving about 335 clients, and 74% of their portfolio was rural.

Risk is uneven on the ground too. In March 2026 Sa-Dhan recorded PAR 30-179 of 3.14% in Gujarat against 0.66% in Assam, while old arrears above 180 days reached 23.5% in Odisha. The RBI's June 2025 Financial Stability Report had shown stressed assets rising from 4.3% to 6.2% between September 2024 and March 2025. With the RBI's 50% repayment cap, lenders need household-level evidence, not just district averages.

The challenges

Where productivity is lost today

01

District and village over-lending

Portfolios cluster in a small set of districts where many lenders compete for the same women borrowers. Without a map of lender density, outstanding per household and growth by village, an MFI can keep disbursing into saturated pockets and discover the problem only when PAR rises, as several high-share states have seen.

Solved by

02

Hard-to-read PAR geography

Delinquency reports usually arrive as branch and state tables. They hide the cluster of centres along one river, the block hit by crop failure or the officer route that is slipping. Risk teams lose weeks working out whether arrears are local, seasonal or structural, and recovery effort gets spread too thinly.

Solved by

03

Costly field operations

Loan officers spend much of the day travelling between centre meetings, collection visits and verification calls. Routes are planned from memory, centres are added wherever demand appears, and supervisors have little sight of where staff actually are. This inflates cost per loan and limits how many groups each officer can serve well.

Solved by

04

Weak borrower and address verification

The 2022 RBI framework requires household income assessment and a 50% repayment cap across all loans. Paper forms, unverified addresses and missing house locations make it harder to confirm that the household exists where stated, to detect duplicate or proxy borrowers and to defend underwriting decisions to auditors and lenders.

Solved by

05

Climate and disaster exposure

A large share of microfinance lending supports farm and allied livelihoods in flood, drought and cyclone prone regions. When a disaster strikes, MFIs often cannot say quickly which centres and how much portfolio sit inside the affected area, which slows relief moratoriums, provisioning and communication with funders.

Solved by

06

Expansion without demand evidence

New branches are often opened by following competitors or senior staff knowledge. Without village-level data on households, existing lender presence, road access and livelihoods, MFIs risk opening in saturated areas or in places too sparse to support a branch, while underserved blocks nearby stay unreached.

Solved by

How GLOBEIR helps

Business needs and how we solve them

Each solution starts from a need Microfinance Institutions (MFIs) faces today, then shows how GLOBEIR delivers it and what changes as a result.

01 · The business need

As lending restarts, funders judge MFIs on where they grow, not just how much. Sa-Dhan's March 2026 data show the top five states held 57% of the portfolio and the top 200 districts nearly 68%. State-level MIS reports cannot show which blocks or villages are saturated, so new disbursement can land in the most crowded pockets again.[3]

Geo-Business Intelligence

District and village concentration dashboards

GLOBEIR can join an MFI's loan book with the credit bureau summaries the MFI shares, census household counts and LGD village codes to show outstanding per household, active borrowers and lender count by district, block and village. Saturation thresholds set by the risk team appear as colour bands, so credit committees can pause, slow or encourage disbursement in specific areas instead of whole states.

  1. 1Geocode the loan book and centres to LGD district, block and village codes.
  2. 2Join census households with the MFI's own bureau summaries to compute lenders per household.
  3. 3Publish dashboards with risk-team saturation thresholds and refresh them on a monthly cycle.

The result

Portfolio limits are set village by village, reducing growth into over-lent pockets.

02 · The business need

Asset quality turned quickly in the last cycle. The RBI's June 2025 Financial Stability Report showed microfinance stressed assets (31-180 days past due) rising from 4.3% in September 2024 to 6.2% in March 2025. Monthly branch tables flagged the stress late and could not show whether it was local, seasonal or spreading across neighbouring centres.[4]

Monitoring Systems

PAR heatmaps and early-warning monitoring

A monitoring dashboard maps PAR 1-30, 31-60 and 61-90 by centre and branch every day or week, with hotspot statistics that flag clusters rather than single accounts. Risk managers can filter by product, loan cycle or officer, and see whether arrears follow a river basin, a market town or one route, then direct audits and recovery staff to the right places.

  1. 1Link daily or weekly repayment data from the loan system to geocoded centres.
  2. 2Run hotspot statistics on PAR buckets to flag clusters across branch boundaries.
  3. 3Configure alerts and drill-down views for risk, audit and regional operations managers.

The result

Emerging delinquency clusters are spotted and acted on earlier.

03 · The business need

As lending restarts after two years of contraction, MFIs must serve each centre at lower cost while proving fair conduct. The RBI's 2022 framework bars recovery contact before 9 a.m. or after 6 p.m. and asks lenders to monitor field staff conduct. Routes planned from memory and paper visit logs give neither efficiency nor evidence.[1]

Live Tracking

Field staff tracking and collection route planning

GLOBEIR delivers live tracking of loan officers on mobile devices, linked to the centre meeting schedule. Network analysis on the road layer sequences centre meetings and collection visits into practical daily routes and balances centre allocation across officers. Supervisors see planned versus actual visits, while time-stamped locations support conduct rules such as the RBI's bar on recovery calls outside 9 a.m. to 6 p.m.

  1. 1Deploy live tracking on officer devices, limited to working hours and meeting schedules.
  2. 2Sequence daily centre and collection visits using network analysis on the road layer.
  3. 3Report planned versus actual visits and time-stamped logs to branch supervisors.

The result

Less travel per visit and more centres covered per officer day.

04 · The business need

The RBI framework limits microfinance to households earning up to Rs 3 lakh a year and caps total loan repayments at 50% of monthly household income. Lenders must assess and record household income, and auditors expect proof. Paper forms and unverified addresses make that evidence weak and leave room for duplicate or proxy borrowers.[1]

Field Validation

Geo-tagged borrower verification with My GLOBEIR

The My GLOBEIR mobile survey app captures house location, household composition and income details at onboarding, working offline-first and syncing automatically. Each geotagged photo carries latitude, longitude, timestamp, GPS accuracy and officer ID. GLOBEIR's analysis then compares each visit's captured GPS position with the declared address and flags applications that share a location, so credit teams can review possible duplicate or proxy borrowers before sanction.

  1. 1Configure My GLOBEIR forms for household, income and geotagged house photos, captured offline.
  2. 2Analyse captured GPS positions against declared addresses and flag applications sharing a location.
  3. 3Sync reviewed records to the loan management system for credit assessment.

The result

Faster, more defensible KYC and income checks with fewer repeat visits.

05 · The business need

A climate policy brief reported that about 60% of the microfinance portfolio funds agriculture and allied activities, much of it in flood, drought and cyclone prone regions. Funders and rating agencies increasingly ask about climate exposure, yet most MFIs still estimate disaster impact by phoning branches, which takes weeks and misses affected centres.[6]

Remote Sensing

Disaster and climate exposure mapping

Satellite flood extents, cyclone tracks and drought indicators from sources such as ISRO's Bhuvan disaster services are overlaid on centre locations. Within days of an event the MFI can list affected centres, borrowers and outstanding, plan relief moratoriums and brief lenders. Over time, historical hazard layers feed exposure limits for flood-prone blocks and seasonal collection calendars.

  1. 1Overlay Bhuvan and satellite hazard layers on geocoded centres and borrower locations.
  2. 2Produce affected-portfolio lists within days of a flood, cyclone or drought event.
  3. 3Build historical hazard scores per block to inform exposure limits and calendars.

The result

Same-week estimates of portfolio affected by a disaster, instead of weeks of branch calls.

06 · The business need

MFIN's June 2026 data show the industry growing again, with first-quarter disbursements of Rs 61,718 crore, 8.9% above a year earlier, but growth is led by larger loans to existing borrowers. To widen reach, MFIs need underserved areas rather than more lending in crowded districts, and expansion based on competitor moves cannot weigh demand, saturation, access and hazard together.[2]

Data Science

Branch site selection and underserved-area scoring

GLOBEIR's data science team can score every block or village on unserved households, lender presence, road travel time from candidate branch sites, livelihood mix and hazard exposure. Catchment models then test how many centres a new branch could reach within a set travel time, so expansion plans favour underserved districts that can support a sustainable branch.

  1. 1Score blocks and villages on unserved households, lender presence, livelihoods and hazard.
  2. 2Model travel-time catchments from candidate branch sites over the road network.
  3. 3Rank sites in a shortlist report for the expansion and credit committees.

The result

Expansion capital goes to locations with evidence of unmet, serviceable demand.

07 · The business need

Risk varies sharply by place: Sa-Dhan's March 2026 data show PAR 30-179 of 3.14% in Gujarat against 0.66% in Assam, and 10 large districts above 3%. Scorecards built only on borrower and bureau attributes miss local signals such as lender crowding, neighbouring arrears, flood history and crop stress, which often show before an individual borrower misses a payment.[3]

Machine Learning

Location-aware credit risk models

Spatial features such as local lender density, recent PAR in neighbouring centres, distance to markets, flood history and crop condition indices can be added to an MFI's existing scorecards. Machine learning models trained on past loan performance test whether these features improve early detection of default risk, with results explained to credit teams at village level.

  1. 1Engineer spatial features from lender density, neighbouring PAR, hazard and crop indices.
  2. 2Train and back-test models on the MFI's past loan performance data.
  3. 3Hand over explainable village-level risk scores for integration with existing scorecards.

The result

Sharper risk ranking of new and repeat loans using where the borrower lives.

In depth

Problem, solution, benefits and data security, topic by topic

Each topic below explains the evidence, the business problem it creates, how GLOBEIR solves it, the benefits to your organisation, and how your data stays private and secure.

  1. 1. What the Evidence Shows
  2. 2. Field Operations and Collections
  3. 3. Portfolio Risk: PAR and Disaster Exposure
  4. 4. Expansion Planning

1. What the Evidence Shows

A Sa-Dhan and M2i study of 50 small MFIs (Dec 2020) found that GPS location capture and live photos in client-origination apps significantly improve the integrity of client sourcing [9]. Yet adoption among small MFIs was low:

Practice Share of small MFIs using it
Mobile app for loan sourcing 38%
Geotagging during client verification 22%
Recording collections on a mobile app 49%
GPS location of centre meetings 24%
In-app client attendance 31%
Tracking field-officer distance travelled 7%

The same study describes how larger MFIs use mobile apps for operational control: tracking field-officer distance, flagging suspicious transactions based on the GPS location of the transaction, and letting supervisors and auditors navigate independently to the client's home or centre [9].

2. Field Operations and Collections

  • Centre and client mapping: every centre, group and client plotted, so branch managers see coverage, distances and overlaps.
  • Collections allocation and routing: cases can be auto-allocated by days past due, risk profile and customer location, and field-officer routes optimised [10].
  • Proof of visit: geotagged, time-stamped centre-meeting and collection records create an audit trail [9].

Business problem

  • Sourcing integrity: without geotags and live photos, ghost, duplicate or mis-located clients are hard to catch, yet only 22% of small MFIs geotag during client verification [9].
  • Unverified centre meetings: only 24% record the GPS location of centre meetings and 31% capture attendance in the app [9], so head office relies on paper registers.
  • Field cost: field-officer travel is a major cost, but only 7% of small MFIs track distance travelled [9]; poorly planned routes waste time that could go into collections.
  • Supervision at scale: as branches grow, supervisors and auditors cannot personally visit every centre.

Our solution

  • A configured My GLOBEIR app for sourcing, client verification, centre meetings and collections, with geotagged live photos, GPS accuracy checks and offline capture.
  • Centre geofences with check-in and attendance, so each meeting is recorded at its expected location.
  • Case allocation by days past due and location, with route planning and field-officer tracking during working hours through Live Tracking.
  • Exception reports for location mismatches, missed centres and duplicate photos, and navigation to client homes or centres for supervisors and auditors [9].
  • Branch, region and head-office views on the Microfinance Map dashboard.

Benefits

  • Stronger sourcing integrity: industry evidence shows GPS capture and live photos significantly improve the integrity of client sourcing [9].
  • Proof that centre meetings and collections took place where and when recorded.
  • Fewer wasted kilometres per collection through location-based allocation and routing [10].
  • Supervision and audit by exception instead of blanket visits.
  • Faster detection of suspicious transactions recorded away from the client's location [9].

Privacy & data security

  • Client home locations, live photos and KYC details are personal data under the DPDP Act [11]. They are encrypted in transit (TLS 1.2 or higher) and at rest (AES-256), and only the roles that need them can see them.
  • Where loans are sourced digitally, the app follows the RBI Digital Lending Directions: need-based data with prior explicit consent and an audit trail, one-time location and camera access for onboarding/KYC, and no storage of biometric data [12].
  • Field-officer location is captured for work purposes. Background tracking is optional and opt-in, with a persistent notification on the device.
  • Offline records stay on the device until connectivity returns, then sync to servers in India.

3. Portfolio Risk: PAR and Disaster Exposure

  • PAR hot-spot mapping. Mapping portfolio-at-risk (PAR30, PAR90) by branch, centre or village reveals geographic concentrations of stress early, before they spread.
  • Concentration limits. Mapping exposure by district and block supports the internal geographic exposure limits that lenders set.
  • Disaster exposure. Overlaying the portfolio on flood, cyclone and drought layers shows how much of the outstanding portfolio lies in hazard zones, supporting pre-disaster planning and moratorium decisions. Satellite flood maps can be produced even under monsoon cloud (see Environment).

Business problem

Portfolio reports usually show PAR by branch in a table, which hides where stress is actually building: a cluster of villages, one block, or the area hit by last month's flood. By the time a branch-level number moves, the problem has often spread. Exposure limits by district and block are hard to monitor without a map, and after a flood or cyclone, management needs to know quickly how much of the portfolio is affected.

Our solution

  • PAR30 and PAR90 hot-spot maps and heatmaps by branch, centre and village, refreshed from the loan management system.
  • District and block exposure maps against the MFI's internal concentration limits, with alerts when a limit is approached.
  • Overlay of the portfolio on flood, cyclone and drought layers, including satellite flood maps after an event, to estimate the outstanding amount in affected areas.
  • Drill-down from region to centre on the Microfinance Map dashboard.

Benefits

  • Earlier, targeted action where stress is clustering, instead of blanket measures.
  • Easier monitoring of district and block concentration limits.
  • Faster, evidence-based decisions on relief, restructuring or moratorium after a disaster.
  • A clear exposure picture for boards, lenders and investors.

Privacy & data security

  • Risk analysis is location- and hazard-based only: it looks at where repayment stress and hazards are concentrated, never at the personal characteristics of borrowers.
  • Hot-spot and exposure analysis runs on aggregated or pseudonymised data at centre, village or block level; client names are not needed.
  • Results stay within the MFI's role-based dashboards, and every access is recorded in audit logs.

4. Expansion Planning

New branch locations can be chosen using village population, existing outlet coverage, competitor presence and road access, in the same way banks plan their networks (see Banking & Finance).

Business problem

Opening a branch commits the MFI to staff, premises and a multi-year portfolio-building effort. Choosing sites by local knowledge alone risks placing branches in areas already crowded with lenders, too far from villages, or in high-hazard zones, which raises both credit risk and operating cost.

Our solution

  • White-space analysis combining village population, existing outlet and competitor coverage, road access and travel time.
  • Hazard overlays (flood, cyclone, drought) so that new branches avoid adding to existing concentration.
  • Ranked candidate locations with catchment maps, shared through the WebGIS portal.

Benefits

  • Branches placed where there is unmet demand and workable travel distances.
  • Less overlap with existing branches and fewer crowded markets.
  • Growth that respects district and block concentration limits.
  • A documented, defensible basis for expansion decisions.

Privacy & data security

  • Expansion analysis uses public and aggregated layers (population, roads, outlets, hazards) and the MFI's own branch locations; no individual client data is required.
  • Planning outputs are commercially sensitive and are shared only with the roles the MFI authorises.

How a project runs

From first data to daily decisions

  1. 1

    Data audit and geocoding

    Collect branch, centre and borrower records, clean addresses and attach LGD village codes and coordinates. Gaps are listed so the field team can capture missing locations with the mobile app during routine centre meetings.

  2. 2

    Base layers and context

    Load census households, administrative boundaries, roads, the credit bureau summaries the MFI supplies and hazard layers into a spatial database, aligned to the same codes so every loan can be compared with its local context.

  3. 3

    Dashboards and analytics

    Build concentration, PAR and exposure dashboards for head office, region and branch users, with agreed thresholds and alerts. Analysts run hotspot, catchment and routing studies on top of the same data.

  4. 4

    Field roll-out

    Deploy the My GLOBEIR app and live tracking to a pilot set of branches, train officers and supervisors, and integrate verification records and visit logs with the loan management system.

  5. 5

    Review and scale

    Compare pilot branches with control branches on visits per day, verification time and early PAR, refine routes and thresholds, then scale region by region with a monthly review of the maps by risk and operations heads.

Data we work with

  • MFI loan book and centre master

    The core layer: loans, repayments, PAR buckets, centres, officers and branches, geocoded to village or house level.

  • Local Government Directory (LGD) codes

    Standard codes for states, districts, sub-districts, blocks and villages that let loan data join cleanly with government datasets.

  • Census of India village and town tables

    Household counts, population and amenities by village, used as the denominator for penetration and saturation measures.

  • Client-supplied credit bureau summaries and industry reports (MFIN, Sa-Dhan)

    Bureau summaries the MFI shares from its own enquiries, plus published district and state benchmarks, show where the wider market is crowded or stressed.

  • ISRO Bhuvan disaster services

    Flood, cyclone, drought and other hazard layers for mapping portfolio exposure before and after events.

  • Satellite imagery (Sentinel, Landsat)

    Crop condition, flood extent and settlement growth indicators for rural areas where ground data is thin.

  • Road network and points of interest

    Roads, markets and bank access points used for route planning, travel-time catchments and branch site selection.

  • My GLOBEIR field survey records

    Geotagged verification visits and house photographs with latitude, longitude, timestamp, GPS accuracy and officer ID, captured offline and synced automatically.

KPIs you can track

  • PAR 30+ and PAR 90+ by district, block and centre
  • Outstanding per household and lenders per borrower in each operating district
  • Share of the MFI's portfolio in its top 10 districts
  • Centre meetings and collection visits per officer per day
  • Kilometres travelled per visit and per rupee collected
  • Turnaround time from application to disbursement
  • Share of borrowers with a verified geo-tagged house location
  • Outstanding exposed to mapped flood, drought or cyclone zones

Privacy & data security

How we keep your data private and secure

Microfinance data maps people's lives with great precision: the home of each client, the place where each group meets, the photo taken at the client's door, and the daily route of every field officer. Under the DPDP Act, location traces, geotagged photos and customer records about identifiable individuals are personal data [11]. NBFC-MFIs and their lending partners are regulated by the RBI, so the way this data is collected, stored and shared with vendors is subject to clear rules.

Regulations we design for

  • RBI Outsourcing of IT Services Directions (2023), now part of the Managing Risks in Outsourcing Directions (2025): data stored only in India; details of customer data processed; RBI right to inspect the provider and its sub-contractors; provider liability for breaches; need-to-know access; and the provider must report cyber incidents to the MFI without undue delay so it can report to RBI within six hours. Existing agreements of NBFCs were to be aligned at renewal or by 10 April 2026 [13][14].
  • RBI Digital Lending Directions (2025): need-based data collection with prior explicit consent and an audit trail; only one-time access to camera, microphone or location for onboarding/KYC; no storage of biometric data; data stored only on servers in India [12].
  • RBI KYC Master Direction: GPS coordinates must be captured in video-KYC recordings and watermarked on digital-KYC photos [15].
  • RBI Cyber Security Framework in Banks (2016): where the lending partner is a bank, it must protect customer data whether stored or in transit, including with third-party vendors [16].
  • Digital Personal Data Protection Act 2023: consent that is free, specific, informed and limited to the data needed; the MFI remains responsible for its processors, which act only under a valid contract; reasonable security safeguards; erasure when the purpose is served [11].
  • DPDP Rules 2025: encryption, masking, access control, logging and backups; breach notice to the Data Protection Board with a detailed report within 72 hours; one-year retention of logs. Most of these obligations apply from about May 2027 [17].
  • CERT-In Directions (2022): report listed cyber incidents to CERT-In within 6 hours and keep ICT logs for a rolling 180 days within India [18].

How GLOBEIR protects your data

Safeguard How it works
Data in India Hosted on ISO 27001 / SOC 2-certified cloud infrastructure in India, or in the MFI's own cloud or data centre
Encryption TLS 1.2 or higher in transit; AES-256 at rest
Role-based access Separate roles for field officers, supervisors, branch/region and head-office users, giving need-to-know access
Audit logs Every access and change to client, centre and collection records is logged
Field app controls Background tracking in My GLOBEIR is optional and opt-in with a persistent notification; location is captured for work purposes; offline data syncs when connectivity returns
Data minimisation PAR hot-spot, exposure and expansion analysis runs on masked, pseudonymised or aggregated data
Purpose limitation MFI data is used only for the agreed purpose; not sold or shared; not used to train models for other clients
Retention and deletion Data returned and deleted at the end of the engagement or on request; account deletion completed within 30 days
Audit and assessment support We sign NDAs, follow the MFI's information-security policies and support its security audits and vendor assessments

Your data, your control

  • The MFI owns its data. GLOBEIR processes it only on the MFI's instructions, under a written contract.
  • Data is used only for the purpose agreed in that contract.
  • Choose your deployment: GLOBEIR-managed India-hosted cloud, your own cloud or data centre, or on-premise.
  • At the end of the engagement, data is exported to you and then deleted.
  • An NDA is available before any data is shared.
  • Privacy questions: privacy@globeir.com.

GLOBEIR's platform is designed to help MFIs meet their DPDP Act obligations; compliance remains the MFI's responsibility as Data Fiduciary.

Frequently asked questions

How does GIS help an MFI stay within the RBI's 2022 microfinance rules?

The RBI framework defines microfinance by household income up to Rs 3 lakh and caps loan repayments at 50% of monthly household income across all loans. GIS does not replace credit bureau checks, but geo-tagged verification visits, mapped lender density and village-level outstanding give underwriters and auditors evidence that assessments were made in the field and that growth is not concentrated in over-lent areas.

Is live tracking of loan officers intrusive?

Tracking is limited to working hours and to the meeting and collection schedule, under a policy agreed with staff. It protects officers as well as borrowers: time-stamped visit logs show that meetings happened, help resolve disputes and support conduct rules such as not contacting borrowers outside permitted hours. Supervisors use the data to rebalance workloads, not only to monitor.

Can this work in villages with poor mobile coverage?

Yes. The My GLOBEIR app is offline-first: it captures forms, geotagged photographs and GPS positions without a network and syncs automatically when the device reconnects. Base maps for a branch area can be loaded in advance, so officers can still find centres and record visits in low-signal areas.

What data do we need to start?

A branch and centre list, a loan-level extract with PAR status and any address or village fields are enough for a first concentration and PAR map. GLOBEIR adds administrative boundaries, census and hazard layers. House-level coordinates can then be built up over a few meeting cycles using the mobile app.

How quickly can we assess portfolio hit by a flood or cyclone?

Once centres are geocoded, satellite flood extents or cyclone tracks can be overlaid as soon as they are published, giving a list of affected centres, borrowers and outstanding within days. That supports quicker decisions on moratoriums, relief and provisioning, and clearer updates to lenders and rating agencies.

Learn the methods behind this

Spatial Statistics and Hotspot AnalysisNetwork Analysis and Routing in GISGIS in Disaster ManagementThematic Map TypesGIS and Remote Sensing in AgricultureOpen Geospatial Data Sources

Sources

  1. [1]Master Direction: Reserve Bank of India (Regulatory Framework for Microfinance Loans) Directions, 2022 · Reserve Bank of India, 2022
  2. [2]Press Release: MFIN releases the 58th edition of Micrometer for Q1 FY 26-27 · Microfinance Industry Network (MFIN), 2026
  3. [3]Quarterly Microfinance Report, Q4 FY 2025-26 (quarter ended 31 March 2026) · Sa-Dhan, 2026
  4. [4]Financial Stability Report, June 2025, Chapter I: Macrofinancial Risks · Reserve Bank of India, 2025
  5. [5]Digital Field Applications: Ujjivan Case Study · Accion (Channels & Technology), 2015
  6. [6]Climate shocks strain the microfinance sector, says a policy brief · Mongabay India (reporting a Climate and Sustainability Initiative brief), 2026
  7. [7]Bhuvan Disaster Services · NRSC, Indian Space Research Organisation, 2026
  8. [8]Local Government Directory · Ministry of Panchayati Raj, Government of India, 2026
  9. [9]Use of technology among small MFIs · Sa-Dhan & M2i, 2020
  10. [10]MFI loan origination and collections with location intelligence · Dista
  11. [11]Digital Personal Data Protection Act, 2023 · MeitY, 2023
  12. [12]Digital Lending Directions, 2025 · RBI, 2025
  13. [13]Master Direction on Outsourcing of Information Technology Services · RBI, 2023
  14. [14]Managing Risks in Outsourcing Directions, 2025 · RBI, 2025
  15. [15]Master Direction – Know Your Customer (KYC) Direction · RBI
  16. [16]Cyber Security Framework in Banks · RBI, 2016
  17. [17]Digital Personal Data Protection Rules, 2025 · MeitY, 2025
  18. [18]Directions under section 70B(6) of the IT Act · CERT-In, 2022

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