Duplicate from FinWorkBench/Finch

Co-authored-by: Haoyu Dong <[email protected]>

.gitattributes

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README.md

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+---
+license: cc-by-3.0
+tags:
+- agent
+- workflow
+- multimodal
+- spreadsheet
+- pdf
+- image
+- code
+- finance
+- accouning
+modalities:
+- text
+- spreadsheet
+- pdf
+- image
+- code
+
+configs:
+  - config_name: Finch_Dataset_All
+    data_files:
+    - split: test
+      path:
+        - finch_workflows_test.jsonl
+---
+
+![Finch cover figure](figs/finch_workflow.jpeg)
+
+# Finch: Benchmarking Finance & Accounting across Spreadsheet-Centric Enterprise Workflows
+
+This repository contains the dataset for **Finch**, an enterprise-grade benchmark for evaluating an agent’s ability to work like a skilled finance & accounting expert (work IQ) on real-world professionel workflows.
+
+* **Paper**: _to be added_
+* **Project Page**: https://huggingface.co/datasets/FinWorkBench/Finch  
+* **Code**: _to be added_
+
+---
+
+## Dataset Description
+
+Finch focuses on **messy and long-horizon finance & accounting workflows** that span:
+
+> data entry/import, structuring/formatting, web search, cross-sheet/file retrieval, calculation, financial modeling, validation, translation, visualization, and reporting.
+
+The workflows are derived from **real-world enterprise workspaces** (primarily Enron, as well as corporations in the EUSES Corpus, investment and securities companies, World Bank, Canadian/British government agencies, and more), including:
+
+- Enterprise **email threads** where collaborators naturally describe, discuss, and track workflows
+- Large and messy **spreadsheets** with multimodal artifacts including text, tables, formulas, charts, pivots, images, etc 
+- Interlinked **PDFs and documents** that provide additional business context  
+
+We adopt a three-step workflow labeling process:
+
+1. **Inducing workflow types and instances** from real collaborative context in **enterprise email threads** (Enron Corpus: 500,000 emails from 150 executives and employees).  
+2. **Deriving concrete workflow instances** by analyzing changes across **spreadsheet versions** (15,000 versioned spreadsheets from Enron and EUSES) and designing workflows based on high-quality artifacts from investment and securities companies, World Bank, Canadian/British government agencies, WideSearch, Dabstep, and more.  
+3. **Conductin meticulous expert annotation** of task instructions, input files, and reference outputs, involving hundreds of hours of expert work.
+
+This process yields **172 enterprise-grade workflows—primarily multi-task composite**, involving 1,710 spreadsheets and 27 million cells, capturing the intrinsic **compositional, messy, multimodal, and collaborative nature** of real-world finance & accounting work. In this release, we provide full annotations for the first 72 workflows, with the remaining 100 to be released in a subsequent update.
+
+Experiment results show that even frontier agents (GPT 5.1 Pro and Claude Sonnet 4.5 Pro) solve fewer than 40% of the workflows, revealing a substantial performance gap for real-world enterprise scenarios.
+
+![Results](figs/exp_results.jpeg)
+
+---
+
+## 📁 Dataset Structure
+
+The instruction-tuning corpus is released in **JSONL** format.  
+Each line corresponds to one **workflow-centric example**:
+
+```json
+{
+  "id": "<workflow identifier>",
+  "instruction_en": "<English task instruction for a finance & accounting workflow>",
+  "source_files": ["<input file name>", "..."],
+  "source_files_urls": ["<input file download URL>", "..."],
+  "reference_outputs": {
+    "files": ["<reference output file name>"],
+    "text": "<textual reference output>"
+  },
+  "reference_file_urls": ["<reference output file download URL>"],
+  "task_type": "<task category (e.g., reporting, modeling)>",
+  "business_type": "<business domain (e.g., budgeting, trading)>"
+}
+```
+---
+
+## 📣 Feedback & Issues
+
+If you find any issues with the dataset or have suggestions, please open a discussion in the **Community** tab — we value your feedback!
+
+**📧 Contact:** [email protected]

files/0/0.json

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+{
+  "id": "0",
+  "instruction_en": "Complete the validation and indicator calculations as follows: on the Balance Sheet, add a control to ensure TOTAL ASSETS equals TOTAL LIABILITIES AND EQUITY; on the Income Statement (Revenue & Expenses), add an Equity Roll Forward Test to reconcile equity movement and highlight any differences.",
+  "source_files": [
+    "0_src_0.xlsx"
+  ],
+  "source_files_urls": [
+    "https://huggingface.co/datasets/FinWorkBench/Finch/resolve/main/files/0/0_src_0.xlsx"
+  ],
+  "reference_outputs": {
+    "files": [
+      "0_ref_0.xlsx"
+    ],
+    "text": ""
+  },
+  "reference_file_urls": [
+    "https://huggingface.co/datasets/FinWorkBench/Finch/resolve/main/files/0/0_ref_0.xlsx"
+  ],
+  "task_type": "Validation / Review, Calculation, Structuring / Formatting",
+  "business_type": "Report"
+}

files/1/1.json

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+{
+  "id": "1",
+  "instruction_en": "Compute the “sum of A” and “sum of B” in the last rows of the Income sheet for each Financial Indicator Component, using data from all sheets in the workbook.",
+  "source_files": [
+    "1_src_0.xlsx"
+  ],
+  "source_files_urls": [
+    "https://huggingface.co/datasets/FinWorkBench/Finch/resolve/main/files/1/1_src_0.xlsx"
+  ],
+  "reference_outputs": {
+    "files": [
+      "1_ref_0.xlsx"
+    ],
+    "text": ""
+  },
+  "reference_file_urls": [
+    "https://huggingface.co/datasets/FinWorkBench/Finch/resolve/main/files/1/1_ref_0.xlsx"
+  ],
+  "task_type": "Calculation, Cross-sheet/file Retrieval",
+  "business_type": "Report"
+}

files/10/10.json

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+{
+  "id": "10",
+  "instruction_en": "Per the headers and established formula logic, populate formulas for columns X through AH so the timing model’s performance statistics for 2013–2025 are complete and consistent with the existing approach.",
+  "source_files": [
+    "10_src_0.xlsx"
+  ],
+  "source_files_urls": [
+    "https://huggingface.co/datasets/FinWorkBench/Finch/resolve/main/files/10/10_src_0.xlsx"
+  ],
+  "reference_outputs": {
+    "files": [
+      "10_ref_0.xlsx"
+    ],
+    "text": ""
+  },
+  "reference_file_urls": [
+    "https://huggingface.co/datasets/FinWorkBench/Finch/resolve/main/files/10/10_ref_0.xlsx"
+  ],
+  "task_type": "Calculation, Financial Modeling",
+  "business_type": "Predictive Modeling"
+}

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files/100/100.json

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+{
+  "id": "100",
+  "instruction_en": "Update the Canada Non-Commercial functional distribution table, ensuring the table remains aligned with the latest staffing data in Sheet2. Additionally, add two columns — Total and %Total — to display the total headcount and percentage for each group.",
+  "source_files": [
+    "100_src_0.xlsx"
+  ],
+  "source_files_urls": [
+    "https://huggingface.co/datasets/FinWorkBench/Finch/resolve/main/files/100/100_src_0.xlsx"
+  ],
+  "task_type": "Structuring / Formatting, Calculation, Validation / Review",
+  "business_type": "Operational Management"
+}

files/101/101.json

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+{
+  "id": "101",
+  "instruction_en": "Standardize all date entries in the spreadsheet to the 01-FEB-2002 format (dd-MMM-yyyy). Month headers should read consistently as 01-JAN-2002, 01-FEB-2002, etc. Pay attention to case sensitivity.",
+  "source_files": [
+    "101_src_0.xlsx"
+  ],
+  "source_files_urls": [
+    "https://huggingface.co/datasets/FinWorkBench/Finch/resolve/main/files/101/101_src_0.xlsx"
+  ],
+  "task_type": "Structuring / Formatting",
+  "business_type": "Trading and Risk Management"
+}

files/102/102.json

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+{
+  "id": "102",
+  "instruction_en": "Create a new Transfer worksheet that summarizes LONGS, SHORT, and NET by delivery month, with Total Longs, Total Shorts, and Total Net at the bottom; the month list should follow the Z, F1… sequence covering all months present, and show 0 for months with no data. In Access Trades, add a side display for TOTAL LONG and TOTAL SHORT with two Notes columns to confirm these tie to the Transfer totals (“ok” if consistent, otherwise note the variance), and return the updated workbook including the Transfer summary and the Access Trades validation block.",
+  "source_files": [
+    "102_src_0.xlsx"
+  ],
+  "source_files_urls": [
+    "https://huggingface.co/datasets/FinWorkBench/Finch/resolve/main/files/102/102_src_0.xlsx"
+  ],
+  "task_type": "Structuring / Formatting, Calculation, Validation / Review",
+  "business_type": "Trading and Risk Management"
+}

files/103/103.json

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+{
+  "id": "103",
+  "instruction_en": "On Sheet4, create two monthly, time-aligned summaries: 'Peoples Deal Value' should sum the Mid Value by month from the 'Peoples Baseload Sale' and 'Transport' sheets (same-month totals), with months without data shown as 0 and a grand total at the end; 'PERC Deal Value' should sum the Mid Value by month from the 'PERC' sheet, with months without data shown as 0. Ensure the month sets for both tables are aligned and sorted chronologically.",
+  "source_files": [
+    "103_src_0.xlsx"
+  ],
+  "source_files_urls": [
+    "https://huggingface.co/datasets/FinWorkBench/Finch/resolve/main/files/103/103_src_0.xlsx"
+  ],
+  "task_type": "Structuring / Formatting, Calculation",
+  "business_type": "Pricing and Valuation"
+}

files/104/104.json

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+{
+  "id": "104",
+  "instruction_en": "Insert a 'Total Available' row and a 'Total Allocated' row, then complete the reconciliation line to verify that 'Total Available' and 'Total Allocated' are consistent.",
+  "source_files": [
+    "104_src_0.xlsx"
+  ],
+  "source_files_urls": [
+    "https://huggingface.co/datasets/FinWorkBench/Finch/resolve/main/files/104/104_src_0.xlsx"
+  ],
+  "task_type": "Structuring / Formatting, Validation / Review, Calculation",
+  "business_type": "Operational Management, Predictive Modeling"
+}

files/105/105.json

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+{
+  "id": "105",
+  "instruction_en": "Add the 2/11/2000 column on the Feb 00 tab by mirroring the 2/10 data, with the following adjustments: Houston Pipe Line should be 6,000 (instead of 9,000), and for Huntsville and Woodlands use 1,000 and 5,000 respectively. All other items remain unchanged from 2/10.",
+  "source_files": [
+    "105_src_0.xlsx"
+  ],
+  "source_files_urls": [
+    "https://huggingface.co/datasets/FinWorkBench/Finch/resolve/main/files/105/105_src_0.xlsx"
+  ],
+  "task_type": "Data Entry / Import, Structuring / Formatting",
+  "business_type": "Operational Management, Predictive Modeling"
+}

files/106/106.json

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+{
+  "id": "106",
+  "instruction_en": "On Sheet3, append the month-over-month percentage changes for BSCTMP and SBSK, then compute the Pearson correlation between their monthly price change rates.",
+  "source_files": [
+    "106_src_0.xlsx"
+  ],
+  "source_files_urls": [
+    "https://huggingface.co/datasets/FinWorkBench/Finch/resolve/main/files/106/106_src_0.xlsx"
+  ],
+  "task_type": "Structuring / Formatting, Calculation",
+  "business_type": "Predictive Modeling"
+}

files/107/107.json

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+{
+  "id": "107",
+  "instruction_en": "On Sheet3, insert on the left side of the sheet a results table with a top-header showing the series names “BSCTMP” and “SBSK,” and two subsections with left-hand labels “1–6 month lag” and “1–6 month lead. In the 1–6 month lag section, quantify the timing relationship by calculating Pearson correlations between SBSK and BSCTMP shifted 1–6 months later and entering the resulting coefficients under the headings “1 month lag” through “6 month lag.” In the 1–6 month lead section, calculate correlations between BSCTMP and SBSK shifted 1–6 months later and entering the coefficients under the headings “1 month lead” through “6 month lead.",
+  "source_files": [
+    "107_src_0.xlsx"
+  ],
+  "source_files_urls": [
+    "https://huggingface.co/datasets/FinWorkBench/Finch/resolve/main/files/107/107_src_0.xlsx"
+  ],
+  "task_type": "Calculation",
+  "business_type": "Predictive Modeling"
+}

files/108/108.json

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+{
+  "id": "108",
+  "instruction_en": "Update the data in first source file based on the second source file. ",
+  "source_files": [
+    "108_src_0.xlsx",
+    "108_src_1.xlsx"
+  ],
+  "source_files_urls": [
+    "https://huggingface.co/datasets/FinWorkBench/Finch/resolve/main/files/108/108_src_0.xlsx",
+    "https://huggingface.co/datasets/FinWorkBench/Finch/resolve/main/files/108/108_src_1.xlsx"
+  ],
+  "task_type": "Cross-sheet/file Retrieval, Data Entry / Import",
+  "business_type": "Report, Planning and Budgeting"
+}

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+{
+  "id": "109",
+  "instruction_en": "Calculate the total FTE percentage by region and by business line, and roll these up into a consolidated summary. This should include totals across EWS, EES, EGM, M&A, and EGA for each region (US Energy, Canada, Federal), with an overall summary of the combined FTE percentages.",
+  "source_files": [
+    "109_src_0.xlsx"
+  ],
+  "source_files_urls": [
+    "https://huggingface.co/datasets/FinWorkBench/Finch/resolve/main/files/109/109_src_0.xlsx"
+  ],
+  "task_type": "Calculation",
+  "business_type": "Operational Management, Planning and Budgeting"
+}

files/11/11.json

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+{
+  "id": "11",
+  "instruction_en": "Add a NAV chart above the Strategy Performance table, plotting two series: Rotation NAV and Benchmark. Use a red line with red markers for Rotation NAV and a gray line with gray markers for the Benchmark; start both series at a base value of 1, and set the time axis to show annual ticks.",
+  "source_files": [
+    "11_src_0.xlsx"
+  ],
+  "source_files_urls": [
+    "https://huggingface.co/datasets/FinWorkBench/Finch/resolve/main/files/11/11_src_0.xlsx"
+  ],
+  "reference_outputs": {
+    "files": [
+      "11_ref_0.xlsx"
+    ],
+    "text": ""
+  },
+  "reference_file_urls": [
+    "https://huggingface.co/datasets/FinWorkBench/Finch/resolve/main/files/11/11_ref_0.xlsx"
+  ],
+  "task_type": "Summary / Visualization",
+  "business_type": "Predictive Modeling"
+}

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+  "id": "110",
+  "instruction_en": "Using the daily PGE/SCG spot midpoints, the TW base price, the fuel‑loss factor, the fixed commodity cost (0.0246), and the day’s delivered volumes to PGE and SCG, please calculate Adj TW Per, the net unit spreads at PGE and SCG, the theoretical profit (spread × volume), the daily total profit, and the Astra/TW 70%/30% profit split, and roll these up to a full‑month summary. Among these values, keep four decimal places for Adj TW Per, the net unit spreads at PGE and SCG, and keep two decimal places for all other figures.",
+  "source_files": [
+    "110_src_0.xlsx"
+  ],
+  "source_files_urls": [
+    "https://huggingface.co/datasets/FinWorkBench/Finch/resolve/main/files/110/110_src_0.xlsx"
+  ],
+  "task_type": "Calculation, Structuring / Formatting",
+  "business_type": "Planning and Budgeting"
+}

files/110/110_src_0.xlsx

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+version https://git-lfs.github.com/spec/v1
+oid sha256:234d5b57eb7610d8f2e428e3c4777701e27c303e5386d3474cb1101b3f3e22bc
+size 309772

files/111/111.json

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+{
+  "id": "111",
+  "instruction_en": "Recompute and update the entire workbook under the new operating assumptions, without changing the original price and volume inputs (PGE/SCG Midpoint, TW benchmark price, and commodity cost at 0.0246). Reduce the TW→PGE fuel loss factor to 4.0% and change the profit split to Astra 60% / TW 40%, which better reflects joint sharing of market and scheduling risk.",
+  "source_files": [
+    "111_src_0.xlsx"
+  ],
+  "source_files_urls": [
+    "https://huggingface.co/datasets/FinWorkBench/Finch/resolve/main/files/111/111_src_0.xlsx"
+  ],
+  "task_type": "Calculation",
+  "business_type": "Planning and Budgeting"
+}

files/111/111_src_0.xlsx

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+version https://git-lfs.github.com/spec/v1
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+size 313748

files/112/112.json

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+{
+  "id": "112",
+  "instruction_en": "For each record, use the Frequency to place the Rent amount into the corresponding Total Weekly, Total Biweekly, or Total Monthly column, and leave the non-applicable total columns blank. Then roll up the Sub-Total for each frequency and estimate the Annual Total.",
+  "source_files": [
+    "112_src_0.xlsx"
+  ],
+  "source_files_urls": [
+    "https://huggingface.co/datasets/FinWorkBench/Finch/resolve/main/files/112/112_src_0.xlsx"
+  ],
+  "task_type": "Cross-sheet/file Retrieval, Data Entry / Import, Calculation",
+  "business_type": "Operational Management, Accounts Payable and Receivable"
+}

files/113/113.json

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+{
+  "id": "113",
+  "instruction_en": "Update the Active Deals vs Headcount sheet with the monthly Active Deals and headcount (HC), and capture the growth amount and the percentage increase from Sep 99 to Mar 01.",
+  "source_files": [
+    "113_src_0.xlsx"
+  ],
+  "source_files_urls": [
+    "https://huggingface.co/datasets/FinWorkBench/Finch/resolve/main/files/113/113_src_0.xlsx"
+  ],
+  "task_type": "Data Entry / Import, Calculation, Cross-sheet/file Retrieval",
+  "business_type": "Operational Management, Planning and Budgeting"
+}

files/114/114.json

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+{
+  "id": "114",
+  "instruction_en": "Using the trades as the source, identify and fill in the applicable asset and liability items for ENA Corp and ECT Inv, then roll them up into a summary. Please ensure the entries reflect only the portions relevant to each entity.",
+  "source_files": [
+    "114_src_0.xlsx"
+  ],
+  "source_files_urls": [
+    "https://huggingface.co/datasets/FinWorkBench/Finch/resolve/main/files/114/114_src_0.xlsx"
+  ],
+  "task_type": "Data Entry / Import, Cross-sheet/file Retrieval, Calculation",
+  "business_type": "Pricing and Valuation, Trading and Risk Management"
+}

files/115/115.json

@@ -0,0 +1,8 @@
+{
+  "id": "115",
+  "instruction_en": "Please track and compile the average monthly house prices for England, Scotland, Wales and Northern Ireland, for the period from January 2022 to April 2025 (including January 2022 and April 2025). Please cite all the statistics from government websites.\\n\\n[Data Source Note] Please use the UK House Price Index (HPI) data published on GOV.UK / HM Land Registry or ONS official sources. Note: HPI data is periodically revised; data from different publication dates is acceptable as long as sourced from official government websites. For data from February 2024 onwards, rounded values (to nearest thousand) from ONS bulletins are acceptable.\\n\\nPlease output an xlsx file with Sheet1 as the RawData sheet, with the following column names in order: Month, House price in England(Pounds), House price in Scotland(Pounds), House price in Wales(Pounds), House price in Northern Ireland(Pounds)\\nFor the column 'Month', list the month in the format 2022/01, 2022/02.\\n\\nDon't ask me any questions, just output the results according to the columns without omitting cells arbitrarily.\\n\\nBased on the RawData sheet, complete the following 4 subtasks:\\n\\nSubtask 1 - Sheet2_Quarterly_Aggregation: Convert monthly data to quarterly averages by region.\\nColumns: Quarter, England Avg Price, Scotland Avg Price, Wales Avg Price, Northern Ireland Avg Price\\nQuarter format: 2022-Q1, 2022-Q2, etc. Each price value with 2 decimals. Sort chronologically from 2022-Q1 to 2025-Q2.\\n\\nSubtask 2 - Sheet3_Price_Changes: Calculate month-over-month price changes.\\nColumns: Month, England MoM Change (%), Scotland MoM Change (%), Wales MoM Change (%), Northern Ireland MoM Change (%)\\nAll percentages with 2 decimals. Start from 2022/02 (comparing to 2022/01).\\n\\nSubtask 3 - Sheet4_Price_Classification: Classify each region's price level for each month.\\nColumns: Month, England Category, Scotland Category, Wales Category, Northern Ireland Category\\nClassification rules: \\\"Very High\\\" if >= 310,000 (England), >= 200,000 (Scotland), >= 225,000 (Wales), >= 190,000 (Northern Ireland); \\\"High\\\" if >= 300,000 / >= 190,000 / >= 215,000 / >= 180,000; \\\"Medium\\\" if >= 290,000 / >= 180,000 / >= 205,000 / >= 170,000; \\\"Low\\\" otherwise.\\n\\nSubtask 4 - Sheet5_Summary_Statistics: Calculate summary statistics for each region.\\nColumns: Region, Max Price, Max Price Month, Min Price, Min Price Month, Overall Change (%), Peak-to-Current Change (%), Volatility Score\\nOverall Change = (Price in 2025/04 - Price in 2022/01) / Price in 2022/01 x 100 (2 decimals). Peak-to-Current Change = (Price in 2025/04 - Max Price) / Max Price x 100 (2 decimals). Volatility Score = STDEV.P (population standard deviation) of all monthly prices / AVERAGE x 100 (2 decimals). Max/Min Price with 2 decimals.\n",
+  "source_files": [],
+  "source_files_urls": [],
+  "task_type": "Web Search, Data Entry / Import, Structuring / Formatting, Calculation, Summary / Visualization",
+  "business_type": "Report"
+}

files/116/116.json

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+{
+  "id": "116",
+  "instruction_en": "Compare the month-to-month movement direction of ERCOT peak power prices and NYMEX natural gas prices in 2002 (i.e., whether each month is higher or lower than the previous month). Based on the 12 monthly changes in 2002, determine how many months the two prices moved in the same direction and how many months they moved in opposite directions.",
+  "source_files": [
+    "116_src_0.xlsx"
+  ],
+  "source_files_urls": [
+    "https://huggingface.co/datasets/FinWorkBench/Finch/resolve/main/files/116/116_src_0.xlsx"
+  ],
+  "task_type": "Calculation",
+  "business_type": "Pricing and Valuation, Trading and Risk Management"
+}

files/116/116_src_0.xlsx

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+version https://git-lfs.github.com/spec/v1
+oid sha256:2002b8987158f27fb7766ae5a3caa4d2eb116d4e9a0633eae99694ca8c39d8dd
+size 179256