About Us

About Us
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CONTACT US

Contact Info

684 West College St. Sun City, United States America, 064781.

(+55) 654 - 545 - 1235

info@corpkit.com

Data Science and AI are transforming banking and financial services by enabling real-time fraud detection, predictive risk modeling, and personalized customer engagement. AI-driven credit scoring, algorithmic trading, and compliance monitoring enhance decision-making and regulatory adherence, while advanced analytics optimize lending strategies and investment portfolios for sustainable growth

Industry Challenges Today

Banks struggle with data spread across legacy systems, CRMs, core banking, mobile apps, etc.
Inaccurate, duplicate, or outdated data reduces decision-making confidence.
Traditional batch-based systems cannot support speed required for fraud detection or hyper-personalization.
Pressures Stringent regulations like GDPR, PCI DSS, and local banking rules require traceability and governance

Loan Application Processing

Claims Automation

Fraud Detection

Customer 360

Objective

To automate the extraction and processing of information from various documents, including scanned files, PDFs, and handwritten forms, using OCR, Deep Learning, and Gen AI

Challenges

Teams manually extract data from documents, images, and scanned forms slow and error-prone
Legacy OCR/RPA systems break when document formats change or if the layout is non-standard
Difficulties in handling handwritten text, nested tables, multi-page contracts, or noisy scans
Data sits in silos; no real-time integration into decision systems or business processes

Solution

Seamless extraction of data from PDFs, images, handwritten notes, and unstructured text using GenAI and Deep Learning. Learns new formats on the fly; no hardcoding needed for every document type. Understands context across multiple document types; 90%+ precision. Real-time data flow via APIs — enabling faster decision-making and better compliance

Features

  • Utilize NLP and computer vision to identify key fields, entities, and layout structures.
  • Train models on historical documents for context-aware extraction (e.g., identifying policy numbers, addresses, medical codes).
  • Implement feedback loops to improve extraction accuracy over time
  • Enable users to ask questions like: “Show claims over ₹10L where discharge summary was missing”
  • Automatically generate dashboards from extracted document data.
  • Visualize extracted insights in real-time, filtered by department, document type, or location
  • Summarize long documents (e.g., contracts, KYC reports) into key decision points.
  • Automatically classify document types and extract custom fields using prompt-driven models.
  • GenAI co-pilot suggests next steps (e.g., “This document lacks PAN validation — flag for compliance review”)

Results

75% Reduction in processing time
95% Accuracy in Data Extraction
60% Cost savings on operations

Objective

To streamline and accelerate the claims lifecycle (credit protection, insurance, fraud, chargebacks) using intelligent automation, improving customer trust, compliance, and operational efficiency

Challenges

Claims processing is manual and fragmented across multiple systems, causing delays and inconsistencies
Risk models are static, relying on outdated or limited internal data
Data silos restrict unified analysis of claims, fraud patterns, and customer behavior
Limited capability for real-time scoring or fraud detection

Solution

By automating claims processing with machine learning applied to unified structured and unstructured data, organizations can significantly reduce delays and errors. Real-time verification powered by streaming transaction and credit data enhances accuracy and responsiveness. A 360° customer claims view across all data sources improves transparency and decision-making, while continuous model improvements enabled through MLOps, Lakehouse-native ML workflows, and feature stores ensure adaptability, scalability, and long-term efficiency

Features

  • Build ML models to:  Predict claim validity based on historical patterns, Score each claim for risk, urgency, and completeness
  • Integrate OCR/NLP pipelines to extract structured data from scanned forms, receipts, discharge summaries, KYC docs, etc.
  • Detect anomalies in claim amount, policy type, and geolocation vs expected patterns
  • Allow claim officers to query data like: “List all high-value claims pending >5 days from Tier-1 customers”
  • Auto-generate dashboards showing SLA breaches, fraud-prone claims, or bottlenecks in processing
  • GenAI copilots assist agents by summarizing case history, recommending decisions, or auto-drafting rejection/approval notes.
  • Auto-generate emails/SMS for customers regarding claim status in plain language

Results

80% Faster Claims Processing
Single source of truth
Lowering fraud payouts by 25%

3. Fraud Detection

Objective

To prevent financial fraud in real time by detecting anomalous behavior across channels using intelligent, adaptive systems powered by Data Science, Generative BI, and Generative AI

Challenges

Reliance on static, rule-based systems with limited adaptability
High false positive rates causing unnecessary manual investigations
Delayed fraud detection due to batch-based processing
Disconnected data sources limiting recognition of fraud patterns across channels

Solution

By implementing real-time fraud detection using machine learning models on streaming transaction data, organizations can identify risks instantly and accurately. A unified data platform enables holistic analysis across channels, while AI-driven scoring significantly reduces false positives. Automated alerting and workflow integration ensure faster incident response, enhancing both efficiency and fraud prevention effectiveness

Features

  • Build ML models trained on historical fraud patterns and user behaviour.
  • Use graph analytics to detect fraud rings and transaction linkages.
  • Ingest real-time data streams (cards, UPI, mobile banking, ATMs) to enable instant scoring.
  • Score transactions using a fraud probability engine across 50+ risk factors.
  • Fraud analysts ask natural language queries like: “Show recent high-value transactions from flagged accounts across channels.”
  • Auto-generate visual dashboards on fraud hotspots, emerging patterns, and suspicious geos.
  • Drill down instantly into customer journeys, time of attack, and velocity checks
  • GenAI agents create summaries of fraud incidents for case investigators.
  • Auto-generate customer communication drafts (e.g.,freeze notice, suspicious login alert).
  • GenAI copilot supports fraud officers in decision making with next-step suggestions: “This account’s behavior matches 93% of a known mule account cluster.”

Results

70% faster fraud detection
Up to 40% fewer false positives
3x improvement in investigation efficiency

4. Customer 360

Objective

To build a unified, intelligent view of each customer to drive personalized engagement, improved risk management, and lifetime value

Challenges

Fragmented data across CRM, mobile apps, web platforms, and branches
Limited visibility into end-to-end customer journeys
Static segmentation leading to generic, untimely outreach
Poor engagement due to lack of personalized offers and services

Solution

By creating real-time, unified customer profiles across all touchpoints, organizations can leverage AI-driven segmentation and predictive behavior modeling to deliver personalized experiences seamlessly across digital and physical channels. This approach enhances customer satisfaction, improves retention, and drives cross-sell success, unlocking greater value from every interaction

Features

  • Entity resolution and graph models to stitch together multi-channel customer identities.
  • Enrich profiles with behaviour, transaction and sentiment data.
  • Enable predictive scores for churn, lifetime value, and cross-sell likelihood
  • Business users ask natural language queries like: “Show me customers with high NPS but no insurance product”
  • Real-time, personalized dashboards generated instantly from the Lakehouse.
  • Automate insights: “Top 10 high-value customers at risk of attrition
  • GenAI agents generate custom offers based on intent + data. Example: “Hi, based on recent travel spend, here’s a curated travel card with 20% reward boost.”
  • GenAI copilots assist RMs by summarizing 360° profiles and suggesting actions in meetings.

Results

360° Unified view
Increased Conversion rates by 40%
Up to 50% cross-sell/upsell