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Multimodal fraud risk model from earnings call text, audio and SEC filings. 3rd place at Hacklytics 2026.

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fraud-detectionhackathonlightgbmmultimodalnlp

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NameName
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.sec_cache
.sec_cache
 
 
backend
backend
 
 
data
data
 
 
frontend
frontend
 
 
.DS_Store
.DS_Store
 
 
.gitignore
.gitignore
 
 
LICENSE
LICENSE
 
 
Qin_Yang_2019.pdf
Qin_Yang_2019.pdf
 
 
README.md
README.md
 
 
aggregate_calls.py
aggregate_calls.py
 
 
build_labels.py
build_labels.py
 
 
classifier_suite.py
classifier_suite.py
 
 
classifier_suite.py:Zone.Identifier
classifier_suite.py:Zone.Identifier
 
 
compress_calls.py
compress_calls.py
 
 
data_aggregate.log
data_aggregate.log
 
 
data_aggregate.py
data_aggregate.py
 
 
diagnose_coverage.py
diagnose_coverage.py
 
 
encode_sbert.py
encode_sbert.py
 
 
feature_importance.py
feature_importance.py
 
 
feature_importance_rf.png
feature_importance_rf.png
 
 
get_overlapping_ticks.py
get_overlapping_ticks.py
 
 
make_y.py
make_y.py
 
 
prelim_model.ipynb
prelim_model.ipynb
 
 
print_stats.py
print_stats.py
 
 
process_filings.py
process_filings.py
 
 
requirements.txt
requirements.txt
 
 
resolve_cik.py
resolve_cik.py
 
 
sec_filings.py
sec_filings.py
 
 
shrink_utils.py
shrink_utils.py
 
 
suite_results.txt
suite_results.txt
 
 
test_lgb.py
test_lgb.py
 
 
ticker_cik_overrides.json
ticker_cik_overrides.json
 
 
times.png
times.png
 
 
train_models.log
train_models.log
 
 
train_models.py
train_models.py
 
 

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hacklytics26

About

Multimodal fraud risk model from earnings call text, audio and SEC filings. 3rd place at Hacklytics 2026.

Topics

fraud-detectionhackathonlightgbmmultimodalnlp

Resources

Readme
MIT license
Activity

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1 fork
Report repository

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