State-of-the-art models trusted by risk teams

Inscribe’s proprietary ML models have been trained on the largest and most diverse database of real-world financial documents, consistently delivering data risk teams rely on for smart onboarding and underwriting decisions.


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Risk Models you can use today


Understand if documents are fraudulent or not.

Document Classification

Ensure documents meet your business requirements.

Document Verification

Confirm that document details match the information on file.


Accurately extract financial details from documents.

Transaction Categorization

Categorize transaction types to identify risk patterns.


Get a complete picture of an applicant’s financial health.

Why Inscribe

Why risk teams choose Inscribe models

If you’re not quite ready to start using AI Risk Agents, you can use our state-of-the-art machine learning models to detect invisible document fraud signals and get cashflow insights for better, faster risk decisions.

Fraud Detection

Understand if applicant documents are fraudulent or not

Document fraud attempts are getting more sophisticated, but Inscribe uses best-in-class detection models to outsmart bad actors. With a combination of heuristics and deep learning, Inscribe assigns a Trust Score and provides a snapshot of any fraud found, helping you quickly understand the trustworthiness of your applicants and their documents.

Document Classification

Ensure documents meet your business requirements

Sometimes, applicants send the wrong documents. Inscribe lets you know immediately by instantly classifying the document type (bank statement, tax form, financial statement) and subtype (Bank of America, W-2, P&L). Inscribe can even check the document format and ensure it’s in the date range you requested.

DOCUMENT Verification

Confirm that document details match the information on file

Too much friction in your onboarding processes can put you at risk of losing customers. Inscribe streamlines the most tedious parts of your KYB/KYC workflows while helping you maintain compliance. With Inscribe, you can instantly verify that the information on submitted documents, like names and addresses, match the information you have on file.

Document Parsing

Accurately extract financial details from documents

Need to pull key details from documents submitted by your applicants? Inscribe quickly and accurately parses data from financial documents like bank statements, checks, pay stubs, W-2s, business tax returns, and P&L statements. Inscribe even returns a structured list of transactions by customer or bank account.

Transaction Categorization

Categorize transaction types to identify risk patterns

Finding transaction fraud and risk signals is tedious, time-consuming work. But Inscribe categorizes transactions so you can easily identify salary, revenue, overdrafts, withdrawals, self-transfers, loan repayments, and other transaction types in just seconds. Easily search, filter, or recategorize transactions as needed.

Cashflow Analysis

Get a complete picture of an applicant’s financial health

Determining creditworthiness doesn’t need to be a chore. Inscribe helps your team fast-track risk assessments with instant insights into cashflow. Using data from bank statements, pay stubs, Plaid, and MX, you can see an applicant’s revenue or personal income, loans, expenditures, and much more to understand financial health.


With Inscribe, we uncover things we normally wouldn’t be able to find using traditional methods.

Preston Miller
Manager, Fraud and Chargeback

Inscribe saves us time, and enables us to make better credit decisions.

Anurag Puranik
Head of Credit and Risk

I have confidence in Inscribe. It’s a highly valuable tool that we rely on to prevent substantial losses.

Jakob Weyland
Director, Risk Strategy


Easily integrate Inscribe into your workflow


Inscribe’s easy-to-implement API enables you to process documents automatically.

Web App

The Inscribe web app allows you to upload documents and visualize fraud signals.


With Inscribe’s secure portal, you can collect documents directly from customers.

Start your AI journey today

See how Inscribe can help you unlock superhuman performance with AI Risk Agents and Risk Models.


How do machine learning models detect document fraud?
Machine learning models detect document fraud by learning patterns and features from authentic documents and then identifying deviations or inconsistencies in new documents that may indicate fraud.
How accurate are machine learning models in detecting document fraud?
The accuracy of machine learning models in detecting document fraud depends on factors such as the quality and quantity of training data, the complexity of fraud patterns, and the effectiveness of the model architecture. High-quality models can achieve high accuracy rates.
Do machine learning models adapt to new fraud techniques over time?
Yes, many machine learning models for document fraud detection are designed to adapt to new fraud techniques over time. They can be continuously updated and retrained with new data to stay effective against evolving fraud methods.
Can machine learning models detect subtle signs of document fraud that humans might miss?
Yes, machine learning models can often detect subtle signs of document fraud that may be difficult for humans to spot, especially in large datasets or when dealing with sophisticated fraud techniques.
How do machine learning models handle privacy concerns when analyzing sensitive documents?
Machine learning models can be designed with privacy-preserving techniques such as encryption, anonymization, or differential privacy to ensure that sensitive information in documents is protected during analysis.
How can machine learning models be trained for document parsing?
Machine learning models for document parsing can be trained using labeled datasets where the model learns to recognize and extract specific fields from different types of documents. The model's performance improves as it encounters more examples during training.
What role does natural language processing play in document parsing?
Natural language processing (NLP) plays a key role in document parsing by enabling models to understand and extract information from text, such as dates, names, addresses, and other relevant data points.
How do machine learning models categorize transactions?
Machine learning models categorize transactions by analyzing transaction data (such as descriptions, amounts, and dates) and classifying them into predefined categories (such as groceries, utilities, or travel) based on learned patterns and features.
What challenges exist in using machine learning for transaction categorization?
Challenges in transaction categorization include dealing with ambiguous or missing transaction descriptions, adapting to new transaction types, and ensuring accuracy and consistency across different accounts or sources.
What are the benefits of using machine learning for cashflow analysis?
The benefits of using machine learning for cashflow analysis include the ability to automate and improve accuracy in cashflow predictions, identify potential issues early, and provide actionable insights for better financial decision-making and planning.