A field collections agent who spends 3 hours of an 8-hour day in transit is an agent whose effective working capacity is 5 hours. On a team of 12 agents, that is 36 person-hours lost to travel every single day — the equivalent of 4.5 full-time agents producing nothing but kilometres. The Field Collections Coordinator AI plans each agent's daily route as a solved logistics problem — not a list of addresses sorted by DPD, but a sequenced itinerary optimised for time windows, geographic clusters, traffic patterns, and borrower availability — and recalculates in real time when circumstances change.
A field collections agent who spends 3 hours of an 8-hour day in transit is an agent whose effective working capacity is 5 hours. On a team of 12 agents, that is 36 person-hours lost to travel every single day — the equivalent of 4.5 full-time agents producing nothing but kilometres. The Field Collections Coordinator AI plans each agent's daily route as a solved logistics problem — not a list of addresses sorted by DPD, but a sequenced itinerary optimised for time windows, geographic clusters, traffic patterns, and borrower availability — and recalculates in real time when circumstances change.
Why collections routing is harder than standard delivery logistics
A parcel delivery route is a logistics problem: minimise distance between a set of drop points, account for traffic, done. A collections route is a logistics problem with three additional constraints that standard routing engines do not handle. First, time windows: a borrower who works at a construction site from 8 AM to 5 PM is only available before 8 AM or after 5 PM — and a visit during working hours produces a wasted trip and a conduct violation under FPC guidelines (contacting a borrower at their workplace without prior notice). Second, escalation priority: a 90-day DPD borrower must be visited today regardless of where they fall geographically — the route must route to priority accounts first, not to geographically convenient ones. Third, contact history: a borrower who was verbally abusive at the last visit may require a two-agent team, which changes the agent assignment and the route simultaneously.
The Field Collections Coordinator AI holds all three constraints simultaneously — time windows from the borrower's contact history and employment profile, escalation priority from the DPD and promise-to-pay data, and contact notes from previous visit logs — and solves the route as an optimisation problem across the full constraint set, not a simple nearest-neighbour sort.
"An agent's day built from a list of addresses is not a route — it is a sequence. The Field AI builds a route: time-windowed, priority-weighted, traffic-adjusted, and re-optimised when anything changes."
Before and after: one agent's day with and without route optimisation
Without Route OptimisationManual sequence, same 8 accounts
1RajajinagarDepart 08:00 · 42 min to next
2WhitefieldArrive 08:42 · 58 min to next (opposite side of city)
3MalleshwaramArrive 09:40 · 44 min to next
4Electronic CityArrive 10:24 · 72 min to next (peak traffic)
5KoramangalaArrive 11:36 · but borrower only available before 9 AM (works at office) — MISSED VISIT
6YeshwantpurArrive 12:30 · 38 min to next
7HSR LayoutArrive 13:08 · 28 min to next
8JayanagarArrive 13:36 · Day ends 16:00
Total travel: 4h 18minProductive visits: 7 of 81 missed (time window)
With Field AI RouteOptimised · same 8 accounts
1Koramangala (early window)Depart 07:30 — borrower available before 9 AM · 12 min travel
2HSR Layout (cluster)Arrive 07:42 · adjacent to stop 1 · 8 min to next
3Jayanagar (cluster)Arrive 07:50 · 14 min to next
4Electronic City (before peak)Arrive 08:04 · pre-peak traffic · 18 min to next
5Whitefield (east cluster)Arrive 08:22 · 16 min to next
6MalleshwaramArrive 10:15 (after crossing peak) · 18 min to next
7Rajajinagar (cluster)Arrive 10:33 · 14 min to next
8Yeshwantpur (cluster)Arrive 10:47 · Day ends 13:30
Total travel: 1h 40minProductive visits: 8 of 80 missed · 2.5 hrs saved
The optimisation factors: what the Field AI weighs for each route
Borrower time windows
Without AIIgnoredVisits scheduled on geographic convenience · Window violations produce missed visits
With Field AIEnforcedTime windows from contact history and employment profile baked into route · Early-window borrowers visited first
DPD priority weighting
Without AIDPD sort only90+ DPD accounts listed first regardless of geography · Long travel between priority accounts
With Field AIPriority + proximity90+ DPD accounts assigned highest priority within geographic clusters · Visited early in the day without cross-city travel
Traffic time-of-day
Without AINot consideredCross-city routes scheduled during peak traffic · Actual travel time 40–60% above estimate
With Field AIReal-time adjustedBengaluru peak hours (7:30–10:00 AM, 5:30–8:30 PM) factored into routing · East-side accounts before peak, west side after
Geographic clustering
Without AINoneAgent travels across the city between consecutive stops · No awareness of adjacent-account opportunities
With Field AIMicro-clusterAccounts within 500m–2km grouped into clusters · 3–5 consecutive visits without crossing a main road
Real-time recalculation
Without AIFixed listRoute unchanged if borrower is unavailable or an appointment is missed · Agent continues to next address
With Field AILive rerouteBorrower not home → next-nearest account inserted from the queue · No wasted gap time between stops
Fleet view: how 12 agents' routes look on the Field AI dashboard
Field Agent Route Dashboard — Nov 14, 2025 · Bengaluru Team · 12 Agents
06:30 dispatch · Routes optimised overnight · Real-time GPS tracking active
AgentToday's stops (priority colour)VisitsTravelPTP due
R. Subramaniam
81h 38m2 today
P. Anjaneyulu
71h 22m3 today
K. Manjunath
91h 52m1 today
M. Shivakumar (2-agent)
51h 14m2 today
2h 38mTravel time saved for one agent — from 4h 18m to 1h 40m · 8 visits completed vs 7 · Zero missed time windows
+1Additional productive visit per agent per day — from eliminated travel time · × 12 agents = 12 more visits/day
19.4hCombined fleet travel — vs 51.6h manual routing · 32 hours of agent capacity recovered daily
Real-timeRoute recalculated when borrower is not home — next-nearest account inserted from queue, no gap time
Route optimisation is not a navigation feature — it is an agent capacity multiplier
32 additional agent-hours recovered daily, across a 12-agent fleet, is not a scheduling achievement. It is the equivalent of adding 4 full-time field agents to the team without hiring anyone. At the typical field visit-to-collection conversion rate of 45%, 12 additional visits per day produces approximately 5 additional collections per day — collections on accounts that would otherwise have gone unvisited because the agent ran out of day before they ran out of accounts. The Field Collections Coordinator AI does not make agents faster — it makes their time productive. An agent whose route eliminates unnecessary travel does not work harder. They work on the visits that matter, in the order that matters, in the time windows when the borrower is actually available to be visited.
