Digitization & Cognitive Process Automation bank statement analysis & risk scoring for loan processing
Using proprietary computer vision, computational geometry and machine learning algorithms, ZapSkore reads and analyses bank statements and tax returns to build risk scores, in under two minutes.
It extracts and tabulates key information such as salary credits, expenses, Loan EMIs, cheque defaults, etc. from bank statements; and gross incomes, expenses, profits etc. from ITRs. ZapSkore can process PDF files as well as scanned images in jpg or png formats. Using advanced analytical models, it then builds meaningful insights about the loan applicant and assigns a risk score to enable you make better disbursement decisions.
How ZapSkore works
Setting up
ZapSkore integrates with the bank’s workflow using a simple plug-and-play API.
Setting up
Identification
Its region-of-interest detector identifies each element — header information, transaction, debit / credit etc. ZapSkore accepts all formats that are in use by banks in India.
Identification
Extraction
It extracts information across pages without missing a single transaction value.
Extraction
Classification
Based on predetermined rules, it classifies and analyses the data available, into income, expenses, debt etc.
Classification
Integration
ZapSkore integrates with credit bureaus such as CIBIL, Equifax etc to obtain credit scores of the applicant.
Integration
Insights
ZapSkore’s machine learning algorithms produce detailed reports about the creditworthiness of the applicant. It also assigns each applicant a risk score to facilitate decision-making.
Insights
ZapSkore Brochure download
For a more detailed look into the product, features and processes, download our brochure here.
How we achieve near-100% accuracy with ZapSkore
Multiple algorithms for region-of-interest detection
In ZapSkore, there are multiple algorithms working simultaneously on region-of-interest identification, ensuring 100% detection.
De-noising
De-noising systems from simple Otsu methods to deep learning-based segmentation algorithms improve ZapSkore’s extraction quality.
Exclusive algorithms for correcting false predictions
DeepQuanty’s patent-pending algorithms are designed especially to correct false predictions, and guess values in noisy images, at 100% accuracy.
Dual algorithmic journeys for building quorum
It builds quorum using two algorithms for every field. Each algorithm has different deep learning bases and maths for feature extraction methods, number of layers, loss functions etc.
Claim your POC
Take ZapSkore for a spin with our free customised proof-of-concept for banks and financial institutions in India. Fill the form below to claim yours now.
Claim your POC
Take ZapSkore for a spin with our free customised proof-of-concept for banks and financial institutions in India. Fill the form below to claim yours now.
