Tag: AI Document Processing

Yuanli Technology Showcased at Singapore FinTech Festival SFF2025, Driving the Future of FinTech with Visual AI

The 10th Singapore FinTech Festival (SFF 2025) successfully concluded on November 14. Over three days, more than 65,000 attendees from 134 countries gathered in Singapore to witness the latest trends and innovations in global financial technology.

Yuanli Technology presented its two core products — FinAuth, the financial-grade KYC identity verification service, and FinDocX, the intelligent document processing platform — at the event, sharing the latest technological capabilities and industry applications with financial institutions, technology companies, and ecosystem partners from Asia-Pacific, the Middle East, Europe, and North Africa.

FinAuth: Financial-Grade face Verification to Tackle AI-Generated Attack Challenges

During the exhibition, the live demo of FinAuth received strong interest from banks and financial service providers companies.

Powered by a Large Vision Model (LVM), FinAuth achieves financial-grade security in ID OCR, liveness detection, and face comparison — accurately defending against AI-generated face attacks (deepfake) with an accuracy rate of over 99%, blocking more than 1 million attack attempts per month.

The ability to detect AIGC deepfake content was one of the most discussed topics among exhibitors this year.

The Yuanli Technology team introduced the latest progress of FinAuth in deepfake detection and model-based risk control strategies. Multiple financial institutions requested on-site testing and initiated collaboration discussions during the event.

FinAuth has already been deployed in banking, cross-border finance, mobility, and social entertainment scenarios — helping enterprises enhance user experience while complying with global regulatory standards such as ISO/IEC 30107, ISO/IEC 27701, ISO/IEC 27001, and GDPR, building a trusted identity ecosystem.

FinDocX: Intelligent Document Processing to Enhance Financial Process Automation

As another highlighted product, FinDocX leverages large AI models to accurately parse complex document formats, automatically extract and verify key business fields, and support multi-language, multi-layout, and multi-type scenarios.

During the exhibition, many visitors brought real business materials — contracts, invoices, identity documents, etc. — for live testing. FinDocX’s automatic recognition and structured output capabilities received wide recognition, helping enterprises achieve faster and lower-error business processes.

During the three-day event, the Yuanli Technology booth attracted a large number of professional visitors from around the world.

Through live deepfake detection demos, document parsing showcases, and 1-on-1 technical exchanges, we established further communication and cooperation intentions with multiple international banks, payment technology companies, overseas enterprises, and regulatory bodies.

Although SFF 2025 has come to an end, conversations and collaborations continue. Moving forward, Yuanli Technology will continue to expand its global footprint and work with partners worldwide to build a safer, more efficient, and trustworthy digital identity and intelligent document solution — powered by cutting-edge Visual AI and intelligent document processing technologies.

We also warmly welcome financial institutions, technology companies, and industry partners to connect with us for further discussions on product demos, technical integration, regional rollout, and scenario cooperation opportunities.

January 22, 2026
Six Typical Applications of FinDocX in the Financial Industry

In the financial industry, virtually every business process revolves around documents—from identity verification for account opening, to income proofs for loan approvals, to financial statements and tax filings. Contracts, forms, and supporting materials pile up quickly. Traditional manual processing is not only inefficient and error-prone but also exposes institutions to high operational costs, prolonged customer wait times, and elevated compliance risks.

At the same time, the industry is accelerating into a new era of intelligence and automation. According to McKinsey, by 2030 artificial intelligence could unlock more than $1 trillion in potential annual value across global banking, with document processing being one of the most fundamental and critical components. Intelligent Document Processing (IDP) is now enabling financial institutions to move beyond repetitive manual tasks by transforming unstructured documents into high-value data—driving faster decisions, lower costs, and superior customer experiences.

Against this backdrop, financial institutions are no longer content with simply processing documents. Instead, they seek to unlock business value from them. FinDocX was created in response to this need.

As a next-generation AI-powered intelligent document processing platform, FinDocX can compress days of manual reviews into just hours—or even minutes—helping institutions cut large-scale operating costs, accelerate business decision-making, and strengthen compliance and risk management. More importantly, FinDocX is not just about “replacing manual work.” It is a true business enabler: delivering high-quality structured data directly into core systems to support intelligent matching, automated validation, and anomaly detection. This elevates document processing from a passive support function into an active driver of business growth.

Unlike traditional OCR solutions, FinDocX ensures both efficiency and reliability, seamlessly integrating with core banking, insurance, and investment workflows to serve as a critical engine for digital transformation. Below, we highlight six typical application scenarios where FinDocX unlocks tangible business value.

Six Typical Applications of FinDocX in Financial Services

Automated Customer Onboarding & KYC

Opening an account and completing KYC often takes days, with manual reviews causing inefficiency and errors. FinDocX can recognize and cross-validate multiple document types—including IDs, passports, utility bills, and bank statements—within hours, achieving accuracy rates above 99%.

Mortgage & Loan Application Processing

Loan approvals involve extensive cross-verification of documents, and traditional processes often delay disbursement. With template-free parsing and intelligent data matching, FinDocX can automatically extract key information from application forms, payslips, and tax forms, and perform consistency checks—shortening approval cycles by up to 60% and significantly reducing manual effort.

Passport Parsing, Extraction, and Cross-Validation

Credit Assessment & Risk Management

Pre-lending risk control requires rigorous verification of documents such as credit reports, employment records, and income statements. Leveraging anomaly detection and automated verification, FinDocX rapidly identifies potential risk indicators and generates structured risk-control data, enabling institutions to make more reliable lending decisions.

No Need to Train New Templates, Automatically Adapt to Different Document Layout, and Extract Business-Required Content

Account Maintenance & Service Requests

Routine processes such as address changes, document updates, or service applications are often delayed due to manual verification. Leveraging multi-language, multi-layout parsing, FinDocX can automatically process change request forms, authorization letters, and ID documents—ensuring real-time updates and delivering a smoother customer service experience.

Multi-Language, Multi-Country ID Document Parsing and Extraction

Financial Statement Extraction & Analysis

Balance sheets, income statements, and other reports are complex and unstructured, making manual entry time-consuming. With multimodal high-precision parsing, FinDocX transforms reports into computable data, automatically generating analytical outputs to support forecasting and performance evaluation.

Tax Document Parsing & Filing

Tax filing requires the highest levels of accuracy and compliance. FinDocX precisely parses various tax forms (e.g., corporate tax returns), performs automated validations, and generates compliance-ready outputs—reducing manual input errors, simplifying filing, and lowering compliance risks.

Parsing, Verification, and Output for Long, Multi-Page, and Complexly Structured Document

The financial industry is shifting from manual processing to intelligence-driven operations. With advantages such as zero-training rapid deployment, template-free parsing, and multimodal high-precision recognition, FinDocX empowers institutions to dramatically cut costs, accelerate decision-making, strengthen compliance and risk control, and—most importantly—turn every document into a business asset that drives growth.

Nowadays, leading banks, insurers, and investment firms are adopting FinDocX as a core tool in their digital transformation journey. If you are looking to unlock greater value across onboarding, lending, risk management, finance, and tax processes, visit the FinDocX website or contact us to schedule a personalized demo and experience the new speed and precision of intelligent document processing.

January 21, 2026
Still Manually Extracting Data from Photos, Screenshots, or Scans? FinDocX Delivers Accurate Results in Seconds—with Over 99% Precision
Organizations are now contending with a mounting pile of unstructured data such as documents, contracts, invoices, bank statements, logistics waybills, insurance claims, and even medical receipts. How companies handle unstructured data documents is both time-consuming and inefficient. With unstructured data coming in a multitude of formats and unorganized, separate fragments of data, information, and files, finance, operations, and even risk teams are required to cross verify the information, manually extract the required information from the screenshots, scanned files and photographs. This entire workflow is highly susceptible to human error.

Data-driven organizations utilize intelligent automation to efficiently and accurately extract critical information that is both accurate and precise. The intelligent automation systems are able to use the urgent raw information, directly converting them into structured data insights, which is highly valuable for organizations.

What Is Unstructured Data—and Why Is It So Hard to Handle?

In the realm of enterprise data, structured data (such as neat database tables) is organized and easy to interpret. However, more than 80% of the structured data resides in core business systems. Despite the lack of defined and standardized systems to understand and interpret data, unstructured data holds critical business insights.
- Text-heavy documents: customer emails, contracts, analyze reports, insurance narratives—information hide in language paragraphs.
- Image or bills: bank statements, invoices, logistics receipts, handwritten notes, stamped scans, stamped scans–Interweaving of text and layout.
- Hybrid Documents: PDF documents, car insurance appraisals, screenshots, photographs of excel and word documents– Mixed formats and scattered information distribution.
Basic OCR technologies perform decently with simple and fixed format documents. However, with complex layouts, diversity in formats, and even comprehending different languages, these systems struggle and face a wall:
1. Implicit and irregular structure: Information doesn’t follow a fixed location or format (unlike database fields); content may appear anywhere in a document, with highly variable layouts.
2. Contextual understanding is essential: Pure text recognition is not enough. Real understanding of semantic relationships and document intent is required.
3. Extreme diversity: Varying image quality, interference from stamps/watermarks, complex nested tables, and cross-page associations are common challenges.
FinDocX: Turning “Manual Labor” into Instant Results

FinDocX is an intelligent document information extraction and analysis service, powered by large model (LLM) technology. It brings advanced document understanding and data extraction capabilities, performs without the need of instructions and template files, bringing out both ordered and unordered information. It supports multiple languages, and can be integrated in to core business systems seamlessly. They will be free from menial cognitive tasks and will have the ability to concentrate on higher-value tasks and decision making.

In the past, dealing with unstructured documents felt like assembling a puzzle:

Finance teams manually entered bank transaction details, insurance adjusters compared various hospital bill formats, logistics staff cross-checked waybills from different carriers—slow and error-prone. With FinDocX, these repetitive tasks are handed over to machines. The result? Accurate structured data in just seconds.

For Enterprises, This Means:
- Speed: Upload a document and have key data extracted almost instantly, slashing workflows from days to mere minutes.
- High Accuracy: Automatically identifies key business information, including the amounts, relevant dates, and customer information with over 99% accuracy, therefore reducing the manual workload.
- No Templates Required: Receipts from new hospitals or logistics documents? No worries. FinDocX processes them automatically.
- Easy Integration: The flow of extracted data into your existing systems for reconciliation, risk control, claims, or even analytics will happen seamlessly.
Teams concerned with finance, operations, and risk control are able to redirect their attention from repetitive data entry to analytic tasks and strategic decision making.

Real-World Case: How a Leading Consumer Finance Platform Uses FinDocX for Credit Verification

Business Scenario: A consumer finance platform required users to upload screenshots of credit credentials (e.g., credit reports) to support automated credit risk assessment. They needed to extract payment behavior and credit history data directly from the images to make real-time loan decisions—replacing traditional manual review.

However, existing document extraction tools presented multiple challenges:
- Poor adaptability to dynamic interfaces: Credit credential formats change frequently. Traditional OCR or small model solutions require new annotations and sample training for each interface change—taking 7–14 days on average.
- High cost and high risk: During the lag time for model updates, manual review costs spiked by 200%. Generic models failed to detect over 25% of key fields on non-standard screenshots (e.g., Photoshop-edited or outdated pages), creating major risk control gaps.
- Unsustainable for scaling: Ongoing retraining costs, long deployment cycles, and constant maintenance made traditional solutions incapable of meeting both rapid growth and strict risk control requirements.
By Implementing FinDocX, the Platform Achieved:
- Precision & Efficiency: The accuracy of recognizing critical credit fields stabilized above 99%, preventing manual rechecking and significantly speeding up the review process.
- High Automation: Achieved over 70% automation of the end-to-end processes, improving overall processing efficiency by ~5x, and cutting the manual workload by over 70%.
- Balanced Growth & Risk Control: The volume of loan approvals increased, the turnaround time which was several days was reduced to a few hours, and even with rapid scaling, the delinquency rates remained well below industry averages.
In the Age of Data, It’s Not Just About Speed—Accuracy and Efficiency Matter More. FinDocX has already helped clients in finance, logistics, and insurance cut document processing time from hours—or even days—to just minutes. Moreover, it helps to effectively reduce the number of mistakes made by humans. Be it flagging stacks of bank statements, varying insurance claims, waybills and contracts of mixed and intricate formats; FinDocX allows full process automation. This empowerment allows the team to concentrate on the truly important tasks—insights and decisions.

Experience FinDocX Today

Click here to visit the official website and learn more.
January 21, 2026
From Traditional OCR to Intelligent Document Processing (IDP): A Generational Leap from Character Recognition to Document Understanding
In the crucial process of enterprise digital transformation, the handling of unstructured data has emerged as a critical indicator of an organization’s competitive edge. Authoritative research indicates that over 80% of corporate data exists in unstructured forms, where document processing efficiency directly determines the timeliness and precision of business decision-making. When processing this type of data, the performance differences between conventional OCR technology and next-generation Intelligent Document Processing (IDP) solutions are becoming increasingly pronounced.

Traditional OCR: The Limitations of Rule-Based Recognition

Traditional OCR (Optical Character Recognition) is built on pattern matching, feature extraction, and classification algorithms to recognize characters. In highly standardized documents such as ID cards or invoices, where layout and fields are fixed, OCR can reach an accuracy rate of over 95%.

However, its heavy reliance on predefined rules leads to significant bottlenecks:
- Poor adaptability: Struggles with distortions, rotations, and multi-page PDFs.
- Difficulty with complex layouts: Draws a lot of accuracy when non-standard forms combine text with tables and stamps appear.
- Lack of generalization: Unable to reliably recognize new or unseen formats.
For example, a large industrial supplier has a 7-person team dedicated to processing purchase orders from diverse clients in trade and manufacturing. With formats varying widely and content filled with detailed tables, traditional template-based OCR proved inadequate, forcing the team to rely on manual data entry.

IDP: How AI Overcomes the Three Core Bottlenecks of OCR

While traditional OCR systems struggle with complex documents due to limited adaptability, rigid layout dependency, and poor generalization. Next-generation IDP solutions such as FinDocX, powered by AI multimodal large models, deliver three transformative breakthroughs：
- Robust Dynamic Parsing: Using anti-interference feature extraction and image enhancement, FinDocX accurately processes low-quality documents affected by blurriness, skewing, or obstructions—reducing error rates on unclear invoices by 63%.
- Multimodal Structural Understanding: Deep learning models intelligently segment text, tables, and stamps while identifying semantic and logical relationships (e.g., “quantity → unit price → total” in purchase orders). This enables complex table recognition accuracy rates of 99%+.
- Zero-Shot Transfer Learning: With no need for template training, FinDocX can adapt to new document types using just 2–3 samples. For a logistics enterprise, this reduced manual verification workloads by 77%, even with mixed-language documents.
Visual comparison between traditional OCR and FinDocX output

Three Generational Leaps in Document Technology

From “Accuracy Decline” to “High Precision in Complex Scenarios”

While traditional OCR may reach 95% accuracy in clean, standard documents, error rates jump to 15% or more when dealing with handwritten waybills, blurry shipping labels, or scanned financial contracts.

FinDocX applies advanced anti-noise algorithms that maintain 99%+ accuracy even on challenging documents—such as 30° tilted, 150 DPI bank archives or non-standard credit card application photos—reducing errors by 83%.

From “Text Transcription” to “Semantic Understanding”

Legacy OCR solutions offer only basic image-to-text output. Financial institutions must still manually extract terms from contracts.

FinDocX integrates lightweight models with large multimodal LLMs to understand semantic content—such as interest clauses in loan contracts or disclaimers in insurance forms—and directly outputs structured, machine-readable data.

From “Custom Development” to “Adaptive Learning”

Traditional OCR requires 2 weeks of retraining for each new format, with accuracy fluctuations up to 34%—a serious barrier in fast-changing industries like finance.

In contrast, FinDocX adapts with just 1–2 labeled examples. One insurance client achieved 94% field recognition accuracy on a new “Claims Detail Form” after submitting only a single sample—versus a 2-month development cycle for traditional solutions.

Case Study | Intelligent Loan Statement Processing at a Joint-Stock Bank

In its corporate lending business, a major joint-stock bank faced a major processing bottleneck: every loan required review of the past 12 months of transaction records. The manual team could only handle 20 applications per day, limiting growth.

After deploying FinDocX:
- Efficiency Boost: Processing time per statement dropped from 3 hours to 2 minutes, scaling to over 500 applications per day.
- Accuracy Jump: Counterparty recognition accuracy improved from 82% to 99.3%.
- Agile Business Support: When a new “cross-border e-commerce settlement” product launched, only 5 sample statements were needed for model adaptation—versus 2 months with older tools.
As the bank’s CRO noted:

“FinDocX transformed our transaction parsing from manual labor to intelligent decision-making—and gave us the data agility needed to lead in emerging markets.”

Structured parsing example of a bank statement

As enterprise data volumes grow exponentially, the limitations of traditional OCR are becoming increasingly exposed. Intelligent Document Processing solutions, by building cognitive intelligence systems, represent a shift from “data transcription” to knowledge production. In today’s data-driven business landscape, organizations must upgrade their document processing capabilities—moving from labor-intensive models to intelligent automation. IDP solutions like FinDocX are the ideal technology partner for this transition.

Click here to try FinDocX today.
January 21, 2026