FINHOUND AI

Copilot, but for your back office
x5 productivity
increased
100+ hours
saved every day
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People make mistakes

when people check for mistakes

Messy and complex data

Reports today are complex and may contain thousands of data items and links. Multiple editing iterations and last-minute changes make them time consuming and difficult to manage.

Labour intensive routine

Fatigue, distraction, bad mood, cognitive biases contribute to mistakes during error-checking leaving room for unnoticed issues.

Legal risks and reputational losses

FinTech and Insurance industries are under high risks of mistakes that are difficult to correct and can deal unrecoverable damage to company’s trustworthiness.

FINHOUND is here to help

AI-based Solution to streamline complex labour‑intensive back office processes
100+ hours are saved every day

MassChallenge 2024FinTech Cohort

FinHound was selected to participate in the MassChallenge 2024 FinTech Program. FinHound was one of over 200 startup applications for this year’s program.

During our partnership with MassMutual, we are streamlining their internal labour-intensive processes saving time and money for the insurance company.
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MassChallenge 2024 FinTech Cohort
MassChallenge 2024 FinTech Cohort

How does it work?

Use case: Finalizing Reports for Audit Firms

Data extractionData validationData transformation

Outstanding OCR

FinHound accurately recognises and extracts data from PDFs/scanned documents of any complexity.

Our Optical Recognition Abstract Layer (ORAL) is based on DETR (End-to-End Object Detection with Transformers), state-of-the-art neural networks. ORAL’s features are fine-tuned for tables and various page layouts, improving the precision and versatility of data extraction.
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Matching data

FinHound compares auditing reports across different periods.

Utilizing its own lightweight and efficient Vector database, FinHound analyzes and searches for matches across different documents.
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100+ Automatic Checks

FinHound indicates exactly where inconsistencies, typos and errors are found, pinpointing their location and origin.

Finhound's algorithm checks the consistency of different financial entities taking into account the contextual factors, such as the corresponding timeframes and dependency relationships, ensuring a more comprehensive analysis of financial data.
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Natural Language Processing (NLP)

FinHound’s NLP models detect characteristic financial entities of every company and can be easily adjusted for various industries.

Leveraging the Natural Language Toolkit (NLTK), our system establishes a foundational comprehension of document context, laying the groundwork for subsequent data processing stages.
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Context Understanding

FinHound’s models do not simply sum the numbers within a table but understand the context of the whole document and catch cross-reference mistakes.

Utilizing neural networks with Transformers and attention mechanisms, our platform enhances verification of contextual details in the data.
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Convenient Summary

FinHound generates an annotated summary based on the original documents.

During in-depth sessions with potential clients, we establish their needs and customize the summary in order to streamline their process and save them time.
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F.A.Q

At Finhound, we specialize in streamlining routine labor-intensive processes using OCR, AI, and machine learning. Our solutions focus on optimizing tasks in areas such as:

  • Data Entry: Using OCR to automate manual data entry tasks for increased accuracy and efficiency.
  • Document Analysis: Utilizing AI for seamless extraction of information from various document formats.
  • Data Integration: Streamlining the integration of data across different sources for a unified view.
  • Workflow Automation: Optimizing repetitive tasks through advanced AI and machine learning algorithms.
While Finhound primarily focuses on fintech, insurance, audit, and banking sectors, our programs have the potential to suit other companies facing routine labor-intensive processes.
We have transparent NLP models that can be customized to fit your specific needs.
Absolutely. We use our own FinHound’s Optical Recognition Abstract Layer (ORAL) and can recognise everything from simple Excel files to challenging PDFs and scans.
Yes, we can.
Yes, our system is designed to seamlessly integrate with various data sources.

Utilizing DETR neural networks, FinHound enhances data extraction precision.

By combining NLTK, neural networks with Transformers and attention mechanisms, along with FinHound's proprietary vector database, our AI-platform offers a flexible and transparent solution to verify and enrich data.

Our technology is transparent and not a black box like OpenAI. Therefore, we can easily adapt to regulatory requirements and are currently working on certifications to ensure trust.
No, if you opt out of that. Based on client’s requirements, we also offer an on premise solution with data localization exclusively within your servers.
No, we can fine-tune our model to match your specific use cases and data, ensuring optimal performance.

FinHound excels in understanding context and identifying cross-reference mistakes within financial/etc statements.

Utilizing DETR neural networks, FinHound enhances data extraction precision.

By combining NLTK, neural networks with Transformers and attention mechanisms, along with FinHound's proprietary vector database, our AI-platform offers a flexible and transparent solution to verify and enrich data.