	In Browser
	StumbleUpon
	del.icio.us
	Google
	Google Buzz
	reddit
	LinkedIn

	Facebook
	Twitter
	Linkedin
	E-Mail

Generative AI > Google Gemini API > Accounts Receivable Ageing Agent

Accounts Receivable Ageing Agent

Author: Venkata Sudhakar

Accounts receivable ageing is a fundamental cash flow management tool. Without systematic follow-up on overdue invoices, businesses accumulate bad debt and face working capital shortfalls. The older a receivable gets, the lower its probability of collection.

An Accounts Receivable Ageing Agent uses Gemini AI to analyse outstanding invoices, classify them into age buckets, calculate the at-risk amount, and recommend specific collection actions based on the age and customer risk profile. This helps ShopMax India collect cash faster and reduce bad debt write-offs.

The below example shows how ShopMax India uses an AI agent to generate its weekly AR ageing report with targeted collection actions for each overdue account.

import asyncio
import json
from google.adk.agents import LlmAgent
from google.adk.tools import FunctionTool
from google.adk.runners import Runner
from google.adk.sessions import InMemorySessionService
from google.genai.types import Content, Part

# Outstanding invoices
INVOICES = [
    {"invoice": "INV-2024-0310", "customer": "Ravi Electronics",   "amount": 142000, "due_date": "2024-03-15", "days_overdue": 22},
    {"invoice": "INV-2024-0285", "customer": "Kumar Mobiles",      "amount": 285000, "due_date": "2024-02-28", "days_overdue": 37},
    {"invoice": "INV-2024-0241", "customer": "Kumar Mobiles",      "amount": 195000, "due_date": "2024-02-10", "days_overdue": 55},
    {"invoice": "INV-2024-0198", "customer": "Apex Gadgets",       "amount": 88000,  "due_date": "2024-02-01", "days_overdue": 64},
    {"invoice": "INV-2024-0112", "customer": "Sri Ganesh Mobiles", "amount": 320000, "due_date": "2024-01-15", "days_overdue": 81},
    {"invoice": "INV-2023-0892", "customer": "Sri Ganesh Mobiles", "amount": 175000, "due_date": "2023-12-20", "days_overdue": 107},
]

def analyse_ar_ageing(invoices_json: str) -> str:
    """Classify invoices into age buckets and compute at-risk totals."""
    invoices = json.loads(invoices_json)

buckets = {"0-30": [], "31-60": [], "61-90": [], "90+": []}

for inv in invoices:
        d = inv["days_overdue"]
        if d <= 30:
            bucket = "0-30"
        elif d <= 60:
            bucket = "31-60"
        elif d <= 90:
            bucket = "61-90"
        else:
            bucket = "90+"
        inv["bucket"] = bucket
        buckets[bucket].append(inv)

bucket_totals = {b: sum(i["amount"] for i in items) for b, items in buckets.items()}
    total_outstanding = sum(inv["amount"] for inv in invoices)

# At-risk estimate: 90+ = 50% bad debt risk, 61-90 = 20%, 31-60 = 5%
    at_risk = (
        bucket_totals.get("90+", 0) * 0.50 +
        bucket_totals.get("61-90", 0) * 0.20 +
        bucket_totals.get("31-60", 0) * 0.05
    )

# Customer-level summary
    by_customer = {}
    for inv in invoices:
        c = inv["customer"]
        by_customer.setdefault(c, {"total": 0, "oldest_days": 0, "invoices": 0})
        by_customer[c]["total"] += inv["amount"]
        by_customer[c]["oldest_days"] = max(by_customer[c]["oldest_days"], inv["days_overdue"])
        by_customer[c]["invoices"] += 1

return json.dumps({
        "bucket_totals": bucket_totals,
        "total_outstanding": total_outstanding,
        "at_risk_amount": round(at_risk),
        "by_customer": by_customer,
        "invoices": invoices,
    })

ar_tool = FunctionTool(func=analyse_ar_ageing)

AGENT_INSTRUCTION = """
You are an Accounts Receivable Ageing Agent for ShopMax India finance.
When given outstanding invoices:
1. Call analyse_ar_ageing with the invoice list
2. Show an ageing summary table: bucket (0-30, 31-60, 61-90, 90+), amount, % of total
3. Show customer-level exposure: customer, total outstanding, number of invoices, oldest overdue days
4. State the at-risk bad debt amount and which customers drive it
5. For each customer, recommend a specific collection action based on their ageing:
   - 0-30 days: send payment reminder email
   - 31-60 days: phone call + formal demand letter
   - 61-90 days: credit hold + senior management escalation
   - 90+ days: legal notice / debt collection agency referral
Format as a weekly AR collection report for the finance team.
"""

ar_agent = LlmAgent(
    name="ar_ageing_agent",
    model="gemini-2.0-flash",
    instruction=AGENT_INSTRUCTION,
    tools=[ar_tool],
)

async def main():
    session_service = InMemorySessionService()
    runner = Runner(
        agent=ar_agent,
        app_name="ar_app",
        session_service=session_service
    )
    session = await session_service.create_session(
        app_name="ar_app",
        user_id="finance-team"
    )
    message = Content(parts=[Part(text="Generate AR ageing report for these invoices: " + json.dumps(INVOICES))])
    print("AR Ageing Agent - ShopMax India")
    print("=" * 36)
    async for event in runner.run_async(user_id="finance-team", session_id=session.id, new_message=message):
        if event.is_final_response() and event.content:
            for part in event.content.parts:
                if part.text:
                    print(part.text)

asyncio.run(main())

It gives the following output,

AR Ageing Agent - ShopMax India
====================================
ACCOUNTS RECEIVABLE AGEING REPORT

AGEING SUMMARY
Bucket   Amount        % of Total
------   -----------   ----------
0-30d    Rs 1,42,000   13.5%
31-60d   Rs 4,80,000   45.6%
61-90d   Rs  88,000     8.4%
90+d     Rs 4,95,000   47.0%    HIGH RISK
Total    Rs 10,05,000

AT-RISK BAD DEBT ESTIMATE: Rs 2,82,600
(90+ at 50% risk: Rs 2,47,500 | 61-90 at 20%: Rs 17,600 | 31-60 at 5%: Rs 24,000)

CUSTOMER EXPOSURE
Customer              Total Due    Invoices  Oldest (days)
--------------------  -----------  --------  -------------
Sri Ganesh Mobiles    Rs 4,95,000     2         107d
Kumar Mobiles         Rs 4,80,000     2          55d
Ravi Electronics      Rs 1,42,000     1          22d
Apex Gadgets          Rs  88,000      1          64d

COLLECTION ACTIONS
Sri Ganesh Mobiles (107d, Rs 4,95,000) - LEGAL ACTION
  Both invoices exceed 90 days. Send legal notice immediately.
  Refer to debt collection agency if no payment in 7 days.

Kumar Mobiles (55d, Rs 4,80,000) - CREDIT HOLD
  Phone call today + formal demand letter by courier.
  Place on credit hold - no new orders until Rs 2,85,000+ cleared.

Apex Gadgets (64d, Rs 88,000) - ESCALATION
  Senior management call. Request payment commitment and date.

Ravi Electronics (22d, Rs 1,42,000) - REMINDER
  Send polite payment reminder email with invoice copy attached.

The agent classified Rs 10.05 lakh across four age buckets and calculated Rs 2.83 lakh at risk of becoming bad debt. Sri Ganesh Mobiles - with 107-day overdue invoices - triggered an immediate legal action recommendation, while Kumar Mobiles warranted a credit hold. The tiered collection actions ensure that follow-up intensity matches the urgency of each situation, protecting ShopMax India's cash flow without damaging customer relationships unnecessarily.

Send your comments, suggestions or queries regarding this site to [email protected].