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Financial Services

Use Cases

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Log Management and Cybersecurity Data-Lake

Traditional data storage tools have limitations in cybersecurity and the ability to track the root cause of problems, leading to delays in data analysis.

With Blendata's single log archive using fast, searchable big data technology, offering an interactive threat forensic dashboard, and employing AI/ML to identify suspicious patterns. The result is the ability to track root causes, reduce operational time and infrastructure costs, and detect and predict cyber attacks through unusual behavior.

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Data Archiving-house

Blendata tackles the challenge of inflexible data storage using tapes by introducing Data Archiving-house solution.

This innovative approach automates data connection and replication from multiple sources, storing data in open formats for easy management and analysis without coding. This results in supporting substantial space savings, on-demand data search, cost-effective scaling of compute and storage, and the ability to restore and integrate data without coding.


360-Degree Customer Dashboard

Blendata's Customer 360 solution efficiently addresses the challenge of managing vast, diverse customer data sources by centralizing and cleansing data while offering segmentation, interactive dashboards, and reports for rapid analysis.

This results in the identification of customer lifetime values, personalized shopping experiences, predictive capabilities for re-ordering and cross-selling, and enhanced support team efficiency.


Fraud Detection System

Blendata's Fraud Detection System effectively counters emerging cybercriminal techniques with its AI/ML-powered solution. It aggregates data from multiple sources, learning from historical patterns to swiftly detect and alert security analysts to suspected fraudulent activities in real-time.

The system supports fraud forensics and root cause analysis through searchable data and interactive BI dashboards. As a result, it significantly enhances cyber-risk and fraud protection, reduces costs, minimizes reliance on human resources, and bolsters the security and reliability of digital banking services.


Credit Scoring

Blendata's Automated Credit Scoring addresses the challenge of time-consuming and limited manual credit scoring. It centralizes data and employs advanced credit modeling and data science, including machine learning, for precise credit assessments.

This automated system enhances efficiency, reduces reliance on human resources, and ensures consistency in decision-making. Ultimately, it boosts profitability for financial institutions by increasing successful loans and decreasing non-performing ones.