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PublicationTowards Automated Regulatory Compliance Verification in Financial Auditing with Large Language Models( 2023)
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PublicationSolving Subset Sum Problems using Quantum Inspired Optimization Algorithms with Applications in Auditing and Financial Data Analysis( 2022)Many applications in automated auditing and the analysis and consistency check of financial documents can be formulated in part as the subset sum problem: Given a set of numbers and a target sum, find the subset of numbers that sums up to the target. The problem is NP-hard and classical solving algorithms are therefore not practical to use in many real applications.We tackle the problem as a QUBO (quadratic unconstrained binary optimization) problem and show how gradient descent on Hopfield Networks reliably finds solutions for both artificial and real data. We outline how this algorithm can be applied by adiabatic quantum computers (quantum annealers) and specialized hardware (field programmable gate arrays) for digital annealing and run experiments on quantum annealing hardware.
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PublicationALiBERT: Improved automated list inspection (ALI) with BERT( 2021-08-16)We consider Automated List Inspection (ALI), a content-based text recommendation system that assists auditors in matching relevant text passages from notes in financial statements to specific law regulations. ALI follows a ranking paradigm in which a fixed number of requirements per textual passage are shown to the user. Despite achieving impressive ranking performance, the user experience can still be improved by showing a dynamic number of recommendations. Besides, existing models rely on a feature-based language model that needs to be pre-trained on a large corpus of domain-specific datasets. Moreover, they cannot be trained in an end-to-end fashion by jointly optimizing with language model parameters. In this work, we alleviate these concerns by considering a multi-label classification approach that predicts dynamic requirement sequences. We base our model on pre-trained BERT that allows us to fine-tune the whole model in an end-to-end fashion, thereby avoiding the need for training a language representation model. We conclude by presenting a detailed evaluation of the proposed model on two German financial datasets.
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PublicationAutomatic Indexing of Financial Documents via Information Extraction( 2021)The problem of extracting information from large volumes of unstructured documents is pervasive in the domain of financial business. Enterprises and investors need automatic methods that can extract information from these documents, particularly for indexing and efficiently retrieving information. To this end, we present a scalable end-to-end document processing system for indexing and information retrieval from large volumes of financial documents. While we show our system works for the use case of financial document processing, the entire system itself is agnostic of the document type and machine learning model type. Thus, it can be applied to any large-scale document processing task involving domain-specific extractors.
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PublicationA Hybrid AI Tool to Extract Key Performance Indicators from Financial Reports for Benchmarking( 2019)We present a tool that enables benchmarking of companies by means of automatic extraction of key performance indicators from publicly available financial reports. Our tool monitors companies of interest so that their reports are automatically downloaded as soon as they become available. After tables and paragraphs have been extracted from the documents using a table detection module based on convolutional neural networks, relevant key performance indicators are stored in a central database. The extracted values are finally displayed in a user-friendly web application where the user can compare time series of key performance indicators against arbitrary available companies.
