LenderAnalyzer is the Hyperscience alternative for lenders who need underwriting numbers, not an enterprise IDP program. Upload statements, tax returns and financials, and get cash flow, DSCR, NSF and existing debt back. From $99 a month.
Upload a document to extract
Drop files here or click to upload
Up to 50 files
Uploading...
Upload a bank statement and watch the analysis run live, free, no signup required.
Hyperscience is an enterprise intelligent document processing platform, now sold as Hypercell. It classifies documents, extracts data from forms, scans, handwriting and semi-structured files, sends low-confidence fields to human reviewers, and runs as SaaS, in a private cloud or on premises, including air-gapped environments. Its customer list leans toward the Fortune 500 and federal agencies: American Express, Charles Schwab, MetLife, the Social Security Administration and Veterans Affairs all appear on its site. Its financial services page names loan origination and mortgage processing as use cases, with loan applications, credit reports, financial statements and transaction statements among the documents it handles.
So why does a lender go looking for something else? Three reasons come up on nearly every shortlist. The first is the entry price. Hyperscience has no pricing page (hyperscience.ai/pricing returns a 404), but its own AWS Marketplace listing prices a 12-month Private Cloud Professional contract at $50,000, and that is the floor before volume, services or custom terms. The second is that extraction is not underwriting. Hyperscience turns a bank statement into clean fields. It does not tell you the borrower's true monthly revenue, how many NSF days there were, or whether an existing advance is already debiting the account. Somebody on your side still builds that. The third is effort. An enterprise IDP rollout means document models, workflow design and a staffed review queue, and even competitors who write about Hyperscience say the configuration takes weeks.
Most published Hyperscience alternatives lists come from software directories or from other document AI vendors, and they compare it with ABBYY, UiPath, Rossum and Textract as general extraction engines. Not one of them is written for a lender. This page is. It covers what Hyperscience is genuinely strong at, what it costs as far as anyone can verify, who the realistic competitors are for a US lending team, and where a lending analysis tool is the better purchase.
An enterprise IDP platform and a lending analysis tool both read loan documents, so they end up in the same comparison spreadsheet. They solve different problems. One turns almost any document into accurate fields at very high volume. The other turns a credit file into the numbers a loan decision rests on. Knowing which problem you actually have settles most of the decision.
Hyperscience earns its analyst coverage. Its homepage lists a Leader placement in Gartner's first Magic Quadrant for Intelligent Document Processing (2025) and Leader and Customer Favorite in the Forrester Wave for Document Mining and Analytics Platforms, Q2 2026, plus Leader ratings from four more firms. It reads handwriting and poor scans better than most extraction engines, keeps a human in the loop for fields the model is unsure about, and deploys where most SaaS cannot: it says it is FedRAMP High authorized, and its AWS listing notes support for air-gapped environments. Hyperscience advertises accuracy rates of 99.5 percent. Treat that as vendor marketing measured on the vendor's own tests, but the pattern behind it is real. If you process millions of pages across dozens of document types under strict data residency rules, Hyperscience is built for you.
We requested hyperscience.ai/pricing directly and it returns a 404, and no page on the main site carries a price. The only public figure is on Hyperscience's own AWS Marketplace listing for Hypercell SaaS, which prices one dimension, HS Private Cloud Professional, at $50,000 for a 12-month contract, with 24 and 36 month terms and private offers available. The listing does not say how many pages that buys. User reports are thin and contradict each other. On PeerSpot, a principal data scientist reported roughly $1.50 per page and said most cloud vendors charge around 50 cents; an operations manager at Genpact described a fixed price against a minimum page volume; an insurance automation lead rated affordability two out of five; and an OCR developer called it cheaper than other tools, with a lifetime licensing option. Those are four different contracts, not one price. Ask for the page allowance, overage rate and services fees as separate lines.
This is the gap that sends most lenders elsewhere. A perfectly extracted bank statement is a table of dates, descriptions and amounts. The underwriting work starts after that: separating true revenue from transfers between the borrower's own accounts, counting NSF events and negative balance days, computing average daily balance, spotting recurring loan and advance payments, flagging stacking, and tying the statement back to the tax return. On a mortgage file it is qualifying income, not a pay stub field. Hyperscience gives your team excellent inputs to that work and leaves the work itself to your analysts or your own rules engine. LenderAnalyzer does that step: upload the statements, tax returns and financials, and it returns computed cash flow, DSCR, NSF and negative days, recurring revenue and existing debt, each figure traceable to the page it came from.
Enterprise IDP is a program, not a login. Someone has to define the document types, configure layouts or train models for the ones that are not prebuilt, design the workflow and staff the human review queue. Extend, a competing document AI vendor, writes that Hyperscience implementations often take several weeks to configure and that frequently changing documents need ongoing work. That is a rival's opinion, so weigh it accordingly, but it matches how enterprise IDP rollouts go. For a lender, bank statements are the hardest case: thousands of US banks and credit unions, each with its own layout, and the layouts change without notice. Budget the internal hours and the services line next to the $50,000 floor, or pick a tool that already reads US bank statements and tax returns on the first day.
For a Gartner Leader, the public review footprint is small. Capterra still lists the product under its older name, HyperEXTRACT, at 5.0 out of 5 from a single review, with pricing as contact vendor, no free trial and no free version. PeerSpot shows seven reviews. The AWS Marketplace listing displays 4.6 out of 5 from 54 external reviews, which it pulls from G2. Few of those reviewers are lenders, because Hyperscience's customer base leans toward insurance, government and large financial institutions. Capterra's own list of Hyperscience alternatives is Automation Anywhere (4.4 from 195 reviews, from $9,000 a year), onPhase, Bizagi and Centralpoint, which are RPA, accounts payable and workflow tools. That is not a useful competitive set for a credit team, so do not borrow it.
For a lender the realistic shortlist splits into three groups. Lending specialists: Ocrolus is the closest like-for-like, built around bank statements, pay stubs and tax returns with cash flow and fraud analytics on top, and it publishes no price either. Hyperscaler APIs: Amazon Textract has a dedicated Analyze Lending API that classifies and extracts a mortgage application package, published at $0.07 per page for the first million pages a month and $0.055 after that, and Google Document AI charges $30 per 1,000 pages for its Custom Extractor. Both are cheap per page and both need engineers. Document AI platforms: Docsumo shows no price on its own site, and Infrrd, which sells mortgage document models, shows no dollar figure on its pricing page. Then there is the analysis layer, where LenderAnalyzer sits.
Buy Hyperscience if you are a large bank, servicer or insurer processing very high volumes across many document types, you need on-premises or air-gapped deployment, you have a team to run the program, and $50,000 a year is small against the labor it replaces. Look elsewhere if your volume is a few hundred credit files a month, if the documents are mostly bank statements, tax returns and financial statements, if you need computed underwriting metrics rather than fields, or if you cannot fund an implementation. For scale, the MBA reported that independent mortgage banks earned $1,201 in pre-tax production profit per loan in Q3 2025, so the $50,000 floor alone equals the profit on about 42 loans. LenderAnalyzer starts at $99 a month, self-serve, with volume and enterprise tiers and an API for teams that want the numbers in their LOS.
Pricing checked against vendor sites, AWS price files, Capterra and PeerSpot on 10 September 2026. Confirm current pricing with each vendor before you buy.
Swipe sideways to see the full comparison
| Software | What it is | Published price | Returns underwriting metrics | Best for |
|---|---|---|---|---|
| LenderAnalyzer This page | Bank statement, tax return and financial statement analysis for lenders, app and API | Published, from $99/mo with volume and enterprise tiers | Yes: cash flow, DSCR, NSF and negative days, recurring revenue, existing debt | US lenders that need decision-ready numbers without an IDP program |
| Hyperscience | Enterprise intelligent document processing platform (Hypercell) | None on its own site; AWS Marketplace lists $50,000 for a 12-month Private Cloud Professional contract | No: extracted fields and workflow, the analysis is yours to build | Large enterprises and agencies with very high, varied volume |
| Ocrolus | Lending document automation with cash flow and fraud analytics | None published; sales-gated custom quote | Yes: lending analytics on bank statements, pay stubs and tax returns | Larger lenders with steady volume and in-house decisioning |
| Amazon Textract (Analyze Lending) | AWS API that classifies and extracts mortgage application packages | Published: $0.07 per page up to 1M pages a month, $0.055 after | No: extracted fields only | Engineering teams building their own mortgage pipeline |
| Google Document AI | Google Cloud extraction APIs with custom extractors | Published: $30 per 1,000 pages (Custom Extractor) | No: extracted fields only | Teams already on Google Cloud with developers |
| Docsumo | Document AI platform with lending and CRE document templates | None on its own site; Capterra lists from $25/mo, usage based | No: fields and validation rules | Mid-market teams automating document intake |
| Infrrd | IDP platform with mortgage document models | None published; pricing page shows no dollar figure | Partial: markets loan package review and income analysis | Mortgage lenders and servicers automating file review |
Comparison compiled by LenderAnalyzer from public vendor materials; see the date noted above each table. Competitor names are trademarks of their respective owners; figures may change, so verify current details with each vendor.
Computed deterministically from every extracted transaction, every figure traceable to its source line.
Computed across the full statement period, carried forward day by day.
Deposits vs withdrawals and net flow, broken down month by month.
Every insufficient-funds and overdraft incident counted, with fees totaled.
Recurring deposits grouped into income streams with estimated monthly amounts.
Debits to other lenders and funders detected and totaled per month.
Days below zero across the period, a direct stress signal.
The biggest credits with dates and sources, concentration flagged.
Automatic red and yellow flags your analysts can review in seconds.
Drop in PDFs, scans or photos, one statement or a multi-month package, from any bank.
Every transaction is extracted, then cash flow, balances, income streams, NSF activity and debt payments are computed.
Read the underwriting snapshot, download the Excel report, or pull structured JSON into your LOS via API.
28 lending document types extracted out of the box, build the complete picture of an applicant's financial situation.
Common questions from lending and credit teams.
It depends on why you are leaving. If you need another enterprise IDP platform with on-premises options, ABBYY and UiPath are the usual swaps. If you are a lender and your documents are mostly bank statements, tax returns and financial statements, a lending-specific tool fits better: Ocrolus for large enterprise programs, LenderAnalyzer for teams that want computed cash flow and DSCR self-serve from $99 a month. If you have engineers and want the lowest cost per page, Amazon Textract's Analyze Lending API publishes $0.07 per page.
Hyperscience does not publish pricing on its website; hyperscience.ai/pricing returns a 404. Its AWS Marketplace listing for Hypercell SaaS prices a 12-month Private Cloud Professional contract at $50,000, with 24 and 36 month terms and private offers. User reports on PeerSpot range from about $1.50 per page to a fixed price against a minimum page volume. Expect a custom enterprise quote, and ask for the page allowance, overage rate and services as separate lines.
The enterprise IDP competitors named most often are ABBYY, UiPath, Tungsten Automation (formerly Kofax), Rossum, Instabase, Indico Data, Amazon Textract and Google Document AI. For lending specifically, the relevant competitors are Ocrolus, Infrrd and Textract's Analyze Lending API, plus analysis tools like LenderAnalyzer that compute underwriting metrics instead of only extracting fields. Capterra's Hyperscience alternatives list leans toward RPA and workflow tools, which is not a useful set for a credit team.
Not publicly. Capterra records no free trial and no free version, the Hyperscience site has no self-serve signup or pricing page, and the AWS Marketplace offer is a 12-month contract rather than a trial. Evaluations run through a sales process, usually with a proof of concept on your own documents. If you want to see analysis on a real credit file today, LenderAnalyzer is self-serve: upload a statement at the top of this page.
It can be, at enterprise scale. Hyperscience lists mortgage processing and loan origination among its financial services use cases and handles loan applications, credit reports and financial statements. Its strengths are high-volume extraction, handwriting and strict deployment requirements. What it does not do is calculate qualifying income or cash flow for you. Mortgage lenders below enterprise volume usually get more from a tool that returns computed income and bank statement analysis directly.
Ocrolus is the more lending-specific product. It is built around bank statements, pay stubs and tax returns and adds cash flow analytics and document fraud detection. Hyperscience is a broader platform that extracts almost any document type, including handwriting, with stronger deployment options such as on-premises and air-gapped. Neither publishes a rate card. A lender that only needs credit file analysis usually favors Ocrolus or LenderAnalyzer; an enterprise consolidating many document workflows favors Hyperscience.
Textract is a pay-per-page AWS API with published rates, including an Analyze Lending API at $0.07 per page for the first million pages a month. You call it from code and build the workflow, review queue and analysis yourself. Hyperscience is a full platform with workflow, human review and model management on top of extraction, sold on annual contracts that start at $50,000 on AWS Marketplace. Textract is cheaper per page; Hyperscience needs fewer engineers.
Yes. Hyperscience documents both SaaS and on-premises or private cloud deployments, its AWS listing notes support for air-gapped environments, and its homepage says it is FedRAMP High authorized for government work. That flexibility is one of its real advantages over most document AI vendors and a big reason it wins regulated enterprise deals. A lender that is comfortable with a US-hosted SaaS tool does not need to pay for it.
As of 10 September 2026, Capterra lists Hyperscience under its older name HyperEXTRACT with one review at 5.0 out of 5, PeerSpot shows seven reviews, and the AWS Marketplace listing displays 4.6 out of 5 from 54 external reviews sourced from G2. Those are small samples for an enterprise platform, and few come from lenders. LenderAnalyzer is not listed on the major directories yet, which is why this page shows its price and output in the open instead.
The analysis. Once fields are extracted, an underwriter still needs true revenue net of transfers, average daily balance, NSF and negative days, recurring income, existing loan and advance payments, stacking flags, DSCR and a tie-out between the statements and the tax return. Extraction platforms leave that to your team or your rules engine. LenderAnalyzer returns those computed numbers from the uploaded documents, which is the step that actually shortens time to decision.
How credit teams run these calculations by hand, so you can see exactly what the software automates.
The mid-market document AI shortlist, priced and compared.
The lending-specific competitor, compared on price and output.
The $50,000 floor, reported per-page rates and cost per loan file.
Per-page, per-document and flat-rate quotes in one table.
Classify and analyze the whole credit file, not just extract it.
Computed underwriting metrics over an endpoint, for teams with engineers.
What the closest lending-specific rival charges, as far as it can be verified.
Analyze your first statements free, plans from $99/month, 50% off billed annually.
