CRISIL Machine-Learning Hiring Challenge-19

CRISIL Machine Learning Hiring Challenge'19 organised by Hackerearth

Extract Financial Statement Values

Financial statement analysis is critical to every organization to enable companies to make better economic decisions that yields more income in the future. For a given financial statement, a rulebook is followed to extract the values associated with accrual, audit status, balance sheet, measurement date and pension plans as shown (refer rulebook & sample data here)

Task:

• Build an NLP model which analyzes each document, looks for relevant financial terms (described below)

• The NLP Model should learn on Documents available under Training data folder using Rules listed here

• Apply the same model on Test Data documents to extract relevant financial information

• The results need to be updated & uploaded in the sheet provided: ‘Results.csv’ (download dataset)

Note: No training labels are explicitly available for this problem statement. You will be able to test your model's accuracy by submitting values extracted for Test data.

Data Description:

Columns	Description of Values
Credit Name, State, Security ID, Org ID, FYE	Identifiers from Documents provided to you already in “Results.csv”
Accounting Basis	Identify ‘Basis of Accounting’ as [‘Accrual’, ‘Cash’, ‘Modified Accrual’, ‘Modified Cash’, ‘Regulatory’]
Pension Plan 1 Name*	Pension Plan Identifier for Pension Plan with highest Total Pension Liability (0 if no date is specified)
Pension Plan 1 Measurement Date	Reporting Date for Pension Plan 1 (0 if no date is specified) [DD/MM/YYYY]
Pension Plan 1 Total Pension Liability	Total Pension Plan liability for Pension Plan 1 (0 if no liability is specified) [int64]
Balance Sheet Cash	Total value of Balance sheet-Governmental funds under ‘Cash & Cash Equivalents’ row. This will include all other row items as well which have been highlighted in the rules for cash and cash equivalent. (0 if no balance sheet amount is specified) [int64]
Pension Plan 2 Name*	Pension Plan Identifier for Pension Plan with second highest Total Pension Liability (0 if no date is specified)
Pension Plan 2 Measurement Date	Reporting Date for Pension Plan 1 (0 if no date is specified) [DD/MM/YYYY]
Pension Plan 2 Total Pension Liability	Total Pension Plan liability for Pension Plan 1 (0 if no date is specified) [int64]

*Note: - Map Pension Plan Names to Industry Standards as specified here

Data Volume:

Train Data: 479 Documents, 1.5GB Test Data: 98 Documents, 373.7MB Submission Format: You need to update Results.csv provided here, with your predictions and upload it online. Data Files: Download Dataset (~1.7GB)

Evaluation Metric:

[ML_Model] accuracy_score

Leaderboard score = 100 * mean(accuracy_score[columns])

[Offline] Source Code, Models/Logic used

Leaderboard

Problem Link

Leaderboard Link

Rank 1