Ingesting, harmonizing and reconciling data and its lineage in various financial, legal and operating documents

BACKGROUND

• Loan documents (pay-stubs, W2s, tax forms, banks statements, purchase contracts, fraud reports, etc.) are available in various formats such as scanned paper, PDF machine readable, XBRL/XML, JSON, Word, PPT, Excel CSV
• Firms manually digitize and analyze the documents which is prone to errors and is expensive to scale

KEY FEATURES

• Automatically processes loan documents without error
• Handles all kinds of purchase contracts, payoff statements, pay-stubs etc.
• Powered by 40+ proprietary AI algorithms to triangulate and fix errors
• In-built subject matter expertise and domain specific ontologies
• Automated data analysis and information extraction from contracts
• Pre-built APIs able to access any external business management systems
• Reinforcement learning via machine and human intelligence
• Offers 85% reduction in time & cost over manual finance processes

BACKGROUND

• Financial and legal documents (cash flow statements, balance sheets, contract notes, trade confirmations, NAV estimates, etc.) are available in various formats such as scanned paper, PDF machine readable, XBRL/XML, JSON, Word, PPT, Excel and CSV.
• Firms manually digitize and analyze the documents which is prone to errors and is expensive to scale

KEY FEATURES

• Segregates and automatically processes error-free financial documents
• Provides semantic similarity among trust documents and other agreements
• Accurately reconciles balance sheets, income and cash-flow statements
• Powered by 40+ proprietary AI algorithms to triangulate and fix errors
• In-built subject matter expertise and domain specific ontologies
• Automated data analysis and information extraction from contracts
• Pre-built APIs able to access any external business management systems
• Reinforcement learning via machine and human intelligence
• Offers 85% reduction in time & cost over manual finance processes

BACKGROUND

Data from multiple sources moves through several systems & is transformed, which leads to non-traceability of data’s origins, transformations & errors

KEY FEATURES

• Pre-built library of 500+ transformation rules for various data types
• Proprietary ML and NLP algorithms to “reverse engineer” business rules
• Identifies data lineage via functional and format transformations.
• Time-series graphs to show parameters & transformations at each “hop”
• APIs & user interface for new configuration, rules, schedule & execution
• Metadata management for guidelines, strategies, policies and implementation
• 80% reduction in time cost

BACKGROUND

• Financial & operating documents (e.g., balance sheets, income statements, rent rolls, fund & tax statements) are often available in PDF, JPEG or paper formats
• Firms manually digitize them, which is expensive to scale & results in errors

KEY FEATURES

• Straight through processing with ~98% accuracy & reliability
• Ingests documents in PDF, image, Excel, XBRL, Word, PPT & other formats
• Pre-built APIs to connect with various sources
• Digitizes > 200 pages/hour stores all input data that can be used later
• ML, NLP and graph-based solution to reverse engineer formulas
• 50+ pre-trained proprietary AI-based algorithms to validate values & fix errors
• Pre-built financial ontology rules for mapping output to client specific templates

BACKGROUND

Agreements in PDF or non machine-readable format require manual processing to detect non-compliance, which is expensive & difficult to scale; lack of compliance in agreements expose organizations to various risks.

KEY FEATURES

• Extracts information such as key attributes and entities from contracts
• Possesses prebuilt ontology for NDAs, SoWs, MSAs, etc.
• 45+ pre-trained modules to digitize, validate attributes & fix errors
• Able to search & compare documents; find trends & generate alerts
• Machines learns on “its own” and via a “human loop”
• Ingests documents in PDF, image, Excel, CSV & other formats
• Pre-built APIs to connect to various sources & templates
• Digitizes > 200 pages/hour & stores all input data that can be used later
• Offers up to 85% improvement in time & cost over manual processes

BACKGROUND

• Duplicate payments, non-compliance & overpaid SoWs
• Current reconciliation process is manual, difficult to scale & leads to errors
• Companies lose average between 0.1% & 0.3% of all payments

KEY FEATURES

• Handles all types of proforma, interim, past due and final invoices
• Ingests documents in diverse formats, e.g., PDF, image, Excel, CSV, etc.
• Extracts key attributes from any document scanned by OCR software
• Automatically reconciles invoices and highlights any accounting errors
• Ensures tax compliance by verifying invoices for KYC, FATCA, etc.
• Proprietary domain ontology for invoices, purchase orders and others
• Machines learns on “its own” and via a “human loop”
• Provides vendor-specific dashboard with all invoices & their status
• Digitizes > 200 pages/hour & stores all input data that can be used later
• Offers 85% reduction in time & cost over manual reconciliation processes