# Fuzzy CSV Reconciler Actor for Insurance Claim Registers (`armourylabs/fuzzy-csv-reconciler-actor-for-insurance-claim`) Actor Reconciles insurer bordereaux against internal claim registers: exact and fuzzy key matching, amount tolerance checks, unmatched-row surfacing, and a forwardable mismatch report per run. Deterministic difflib matching — no AI. CSV in, forwardable JSON report out. Built for broker ops and MGAs. - **URL**: https://apify.com/armourylabs/fuzzy-csv-reconciler-actor-for-insurance-claim.md - **Developed by:** [Christopher Smith](https://apify.com/armourylabs) (community) - **Categories:** Automation, Developer tools - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $12,000.00 / 1,000 reconciliation processeds This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours, and optionally produces a well-defined JSON output, datasets with results, or files in key-value store. In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server. Actors are written with capital "A". ## How to integrate an Actor? If asked about integration, you help developers integrate Actors into their projects. You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready. The best way to integrate Actors is as follows. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js/docs.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python/docs.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/integrations/mcp.md). If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt). # README ## Fuzzy CSV Reconciler — Insurance Claim / Bordereau Register A production-grade reconciliation tool for insurance brokers and MGAs who must monthly reconcile insurer bordereaux and claim registers against internal spreadsheets. It finds exact and fuzzy-matched records, flags amount discrepancies, compares additional fields, and surfaces every unmatched row — all in a single structured JSON report you can forward directly to your insurer counterparty or finance team. --- ### Who Buys This | Persona | Pain | |---|---| | Insurance broker ops teams | Monthly bordereau reconciliation done in Excel; mismatches found late, causing payment delays | | MGA finance teams | E&O exposure from undetected claim register discrepancies | | Reinsurance technicians | Ceded/assumed bordereau matching across differently-formatted exports | --- ### What It Does 1. **Ingests two CSV registers** — your internal export (A) and the insurer bordereau (B). 2. **Auto-detects key columns** — claim ID, claim number, policy number, reference, etc. — with no configuration needed for standard column names. 3. **Exact key matching** — identical claim IDs are matched instantly. 4. **Fuzzy key matching** — typos, formatting differences (`CLM-2024-001` vs `CLM2024001`), and abbreviations are reconciled using configurable similarity scoring. 5. **Amount comparison** — flags rows where paid/gross/incurred amounts differ beyond a configurable tolerance (default ±0.01). 6. **Extra field comparison** — optionally compare insured name, status, date of loss, or any named column pair between the two registers. 7. **Unmatched rows** — rows present in A but absent from B (and vice versa) are clearly labelled. 8. **Summary statistics** — exact matches, fuzzy matches, amount mismatches, field mismatches, unmatched counts. #### What It Does NOT Do - Does not read PDF or Excel files (CSV only). - Does not send emails or upload reports automatically. - Does not use AI or machine learning — fuzzy matching is deterministic sequence similarity (Python `difflib`). --- ### Input Fields | Field | Type | Required | Default | Description | |---|---|---|---|---| | `csv_a` | string | ✅ | — | Full CSV text of register A (internal system) | | `csv_b` | string | ✅ | — | Full CSV text of register B (insurer bordereau) | | `key_col_a` | string | No | auto | Column name in A for claim/record ID | | `key_col_b` | string | No | auto | Column name in B for claim/record ID | | `amount_col_a` | string | No | auto | Amount column in A | | `amount_col_b` | string | No | auto | Amount column in B | | `extra_fields` | array | No | `[]` | `[{"col_a": "status", "col_b": "claim_status"}]` | | `fuzzy_threshold` | number | No | `0.80` | 0–1 similarity; lower = more permissive matching | | `amount_tolerance` | number | No | `0.01` | Max absolute difference treated as a match | --- ### Output Structure ```json { "summary": { "total_rows_a": 150, "total_rows_b": 148, "exact_matches": 140, "fuzzy_matches": 6, "unmatched_in_b": 4, "unmatched_in_a": 2, "amount_mismatches": 3, "field_mismatches_rows": 5, "ok_rows": 141 }, "metadata": { "key_col_a": "claim_id", "key_col_b": "claim_number", "amount_col_a": "amount", "amount_col_b": "gross", "extra_fields_compared": [] }, "records": [ { "key_a": "c001", "key_b": "c001", "match_type": "exact", "match_score": 1.0, "status": "OK", "amount_comparison": { "status": "match", "a_amount": 1000.0, "b_amount": 1000.0, "difference": 0.0 }, "field_comparisons": [], "row_a": {"claim_id": "C001", "amount": "1000.00"}, "row_b": {"claim_number": "C001", "gross": "1000.00"} } ] } ```` Each record has status: `OK`, `MISMATCH`, `UNMATCHED_IN_B`, or `UNMATCHED_IN_A`. *** ### Local Demo **Prepare two CSV files as strings and pass via CLI:** ```bash python3 main.py '{ "csv_a": "claim_id,insured,amount\nC001,Acme Corp,1000.00\nC002,Beta Ltd,2500.50\nC003,Gamma Inc,750.00", "csv_b": "claim_number,insured_name,gross\nC001,Acme Corp,1000.00\nC002,Beta Ltd,2499.00\nC004,Delta PLC,300.00", "fuzzy_threshold": 0.80, "amount_tolerance": 0.01 }' ``` Expected highlights in output: - C001 → `OK` (exact match, amounts agree) - C002 → `MISMATCH` (amount differs by 1.50) - C003 → `UNMATCHED_IN_B` (not in bordereau) - C004 → `UNMATCHED_IN_A` (bordereau has extra claim) *** ### Run Tests ```bash pytest tests/test_main.py -v ``` All tests use Python standard library only. No external dependencies required. *** ### Pricing - **Pay-per-run:** $15–40 depending on register size - **Monthly subscription:** $99/mo unlimited runs *** *Generated by the Fuzzy CSV Reconciler — reconciliation reports carry attribution that travels with the artefact.* # Actor input Schema ## `csv_a` (type: `string`): Full CSV text of your internal claim register or bordereau export. Paste the entire CSV including header row. ## `csv_b` (type: `string`): Full CSV text of the insurer or counterparty bordereau. Paste the entire CSV including header row. ## `key_col_a` (type: `string`): Column name to use as the claim/record identifier in Register A (e.g. 'claim\_id', 'policy\_number'). Leave blank for auto-detection. ## `key_col_b` (type: `string`): Column name to use as the claim/record identifier in Register B (e.g. 'claim\_number', 'reference'). Leave blank for auto-detection. ## `amount_col_a` (type: `string`): Column containing claim/payment amounts in Register A (e.g. 'amount', 'incurred', 'paid'). Leave blank for auto-detection. ## `amount_col_b` (type: `string`): Column containing claim/payment amounts in Register B (e.g. 'gross', 'settlement', 'loss'). Leave blank for auto-detection. ## `extra_fields` (type: `array`): Additional column pairs to compare between registers. Example: \[{"col\_a": "status", "col\_b": "claim\_status"}, {"col\_a": "date\_of\_loss", "col\_b": "loss\_date"}] ## `fuzzy_threshold` (type: `number`): Minimum similarity score (0–1) for two record keys to be considered a match. 0.80 is recommended. Lower values match more aggressively; raise to 0.95+ for strict matching. ## `amount_tolerance` (type: `number`): Maximum absolute difference between two amounts that is still treated as a match (e.g. 0.01 for penny-rounding, 1.00 to allow rounding to nearest dollar). ## Actor input object example ```json { "csv_a": "claim_id,insured_name,amount\nDEMO-CLM-001,DEMO SAMPLE Pty Ltd (not a real insured),1000.00\nDEMO-CLM-002,DEMO SAMPLE Pty Ltd (not a real insured),2500.00", "csv_b": "claim_id,insured_name,amount\nDEMO-CLM-001,DEMO SAMPLE Pty Ltd (not a real insured),1000.00\nDEMO-CLM-002,DEMO SAMPLE Pty Ltd (not a real insured),2450.00", "extra_fields": [], "fuzzy_threshold": 0.8, "amount_tolerance": 0.01 } ``` # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "csv_a": `claim_id,insured_name,amount DEMO-CLM-001,DEMO SAMPLE Pty Ltd (not a real insured),1000.00 DEMO-CLM-002,DEMO SAMPLE Pty Ltd (not a real insured),2500.00`, "csv_b": `claim_id,insured_name,amount DEMO-CLM-001,DEMO SAMPLE Pty Ltd (not a real insured),1000.00 DEMO-CLM-002,DEMO SAMPLE Pty Ltd (not a real insured),2450.00` }; // Run the Actor and wait for it to finish const run = await client.actor("armourylabs/fuzzy-csv-reconciler-actor-for-insurance-claim").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs ``` ## Python example ```python from apify_client import ApifyClient # Initialize the ApifyClient with your Apify API token # Replace '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = { "csv_a": """claim_id,insured_name,amount DEMO-CLM-001,DEMO SAMPLE Pty Ltd (not a real insured),1000.00 DEMO-CLM-002,DEMO SAMPLE Pty Ltd (not a real insured),2500.00""", "csv_b": """claim_id,insured_name,amount DEMO-CLM-001,DEMO SAMPLE Pty Ltd (not a real insured),1000.00 DEMO-CLM-002,DEMO SAMPLE Pty Ltd (not a real insured),2450.00""", } # Run the Actor and wait for it to finish run = client.actor("armourylabs/fuzzy-csv-reconciler-actor-for-insurance-claim").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "csv_a": "claim_id,insured_name,amount\\nDEMO-CLM-001,DEMO SAMPLE Pty Ltd (not a real insured),1000.00\\nDEMO-CLM-002,DEMO SAMPLE Pty Ltd (not a real insured),2500.00", "csv_b": "claim_id,insured_name,amount\\nDEMO-CLM-001,DEMO SAMPLE Pty Ltd (not a real insured),1000.00\\nDEMO-CLM-002,DEMO SAMPLE Pty Ltd (not a real insured),2450.00" }' | apify call armourylabs/fuzzy-csv-reconciler-actor-for-insurance-claim --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=armourylabs/fuzzy-csv-reconciler-actor-for-insurance-claim", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Fuzzy CSV Reconciler Actor for Insurance Claim Registers", "description": "Reconciles insurer bordereaux against internal claim registers: exact and fuzzy key matching, amount tolerance checks, unmatched-row surfacing, and a forwardable mismatch report per run. Deterministic difflib matching — no AI. CSV in, forwardable JSON report out. Built for broker ops and MGAs.", "version": "0.1", "x-build-id": "8cB99033ao89QLr5I" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/armourylabs~fuzzy-csv-reconciler-actor-for-insurance-claim/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-armourylabs-fuzzy-csv-reconciler-actor-for-insurance-claim", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/armourylabs~fuzzy-csv-reconciler-actor-for-insurance-claim/runs": { "post": { "operationId": "runs-sync-armourylabs-fuzzy-csv-reconciler-actor-for-insurance-claim", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/armourylabs~fuzzy-csv-reconciler-actor-for-insurance-claim/run-sync": { "post": { "operationId": "run-sync-armourylabs-fuzzy-csv-reconciler-actor-for-insurance-claim", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "required": [ "csv_a", "csv_b" ], "properties": { "csv_a": { "title": "Register A (Internal System CSV)", "minLength": 10, "type": "string", "description": "Full CSV text of your internal claim register or bordereau export. Paste the entire CSV including header row." }, "csv_b": { "title": "Register B (Insurer Bordereau CSV)", "minLength": 10, "type": "string", "description": "Full CSV text of the insurer or counterparty bordereau. Paste the entire CSV including header row." }, "key_col_a": { "title": "Key Column in Register A (optional)", "type": "string", "description": "Column name to use as the claim/record identifier in Register A (e.g. 'claim_id', 'policy_number'). Leave blank for auto-detection." }, "key_col_b": { "title": "Key Column in Register B (optional)", "type": "string", "description": "Column name to use as the claim/record identifier in Register B (e.g. 'claim_number', 'reference'). Leave blank for auto-detection." }, "amount_col_a": { "title": "Amount Column in Register A (optional)", "type": "string", "description": "Column containing claim/payment amounts in Register A (e.g. 'amount', 'incurred', 'paid'). Leave blank for auto-detection." }, "amount_col_b": { "title": "Amount Column in Register B (optional)", "type": "string", "description": "Column containing claim/payment amounts in Register B (e.g. 'gross', 'settlement', 'loss'). Leave blank for auto-detection." }, "extra_fields": { "title": "Extra Field Comparisons (optional)", "type": "array", "description": "Additional column pairs to compare between registers. Example: [{\"col_a\": \"status\", \"col_b\": \"claim_status\"}, {\"col_a\": \"date_of_loss\", \"col_b\": \"loss_date\"}]", "default": [] }, "fuzzy_threshold": { "title": "Fuzzy Match Threshold", "minimum": 0, "maximum": 1, "type": "number", "description": "Minimum similarity score (0–1) for two record keys to be considered a match. 0.80 is recommended. Lower values match more aggressively; raise to 0.95+ for strict matching.", "default": 0.8 }, "amount_tolerance": { "title": "Amount Tolerance", "minimum": 0, "type": "number", "description": "Maximum absolute difference between two amounts that is still treated as a match (e.g. 0.01 for penny-rounding, 1.00 to allow rounding to nearest dollar).", "default": 0.01 } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```