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Traditional AI Case study

Financial Technology (FinTech) Fraud Detection Case Study​

Challenges​

A FinTech company was struggling with real-time fraud detection as the transaction volumes grew. Their current systems failed to maintain high accuracy, which posed a risk of financial losses and reduced customer trust.

Solution by Opstree​

Opstree partnered with the company to deliver a tailored AI-powered fraud detection solution. The solution leveraged Amazon SageMaker for model training and deployment, Amazon Fraud Detector for identifying suspicious activities, and proprietary models that combined supervised and unsupervised learning techniques, along with scikit-learn and PyTorch for custom fraud detection algorithms.

Opstree further integrated Amazon Textract and Amazon Rekognition to strengthen document verification and identity validation processes, while adding advanced clustering techniques to uncover fraud networks. Continuous retraining pipelines were also implemented using Amazon SageMaker, ensuring the model stayed accurate over time.

Results​

Opstree's AI solution led to enhanced fraud detection with fewer false positives and negatives. The real-time detection capabilities and use of advanced services helped the company quickly identify fraudulent patterns and mitigate financial risks.

  • Reduced NPAs from 9% to 6%.
  • Decreased monthly financial losses by ₹2 Cr.
  • Faster fraud detection enabled quick action in high-risk areas.

Tools Used​

  • Amazon SageMaker
  • Amazon Fraud Detector
  • Amazon Textract
  • Amazon Rekognition
  • Python (scikit-learn, PyTorch)

Financial Technology (FinTech) Credit Risk Analysis and Modeling Case Study​

Challenges​

The FinTech company needed to expedite loan approvals while reducing Non-Performing Assets (NPAs). Traditional credit risk methods resulted in slower processing times and suboptimal loan terms, negatively impacting customer satisfaction.

Solution by Opstree​

Opstree developed an AI-based credit risk analysis solution using a combination of supervised models, including logistic regression with PSI/CSI tracking to ensure model accuracy. Using scikit-learn, Opstree trained custom credit risk models and deployed them using Python (FastAPI), which enabled efficient real-time decision-making.

Additionally, Amazon SageMaker was used to automate model retraining, while PowerBI provided detailed analytics and dashboards to track credit risk performance. Opstree also incorporated clustering algorithms to assess borrower risk profiles based on alternative data sources such as transactional patterns and social profiles.

Results​

The credit risk AI solution revolutionized the company's lending operations, improving loan approval speed and accuracy while reducing NPAs. The detailed analytics allowed the company to optimize lending terms and improve customer satisfaction.

  • Reduced NPAs from 9% to 6%.
  • Reduced delinquency rates by 5%.
  • Reduced reliance on external credit scores by 25% through in-house AI models.

Tools Used​

  • Python (FastAPI)
  • scikit-learn
  • Amazon SageMaker
  • PowerBI for analytics

IoT Predictive Maintenance for HVAC Systems Case Study​

Challenges​

An HVAC company needed a proactive maintenance strategy to reduce equipment downtime and operational costs. Their traditional reactive approach led to frequent breakdowns and costly repairs.

Solution by Opstree​

Opstree implemented an AI-driven predictive maintenance solution based on time-series modeling. Using ARIMA models developed with scikit-learn, Opstree analyzed data from IoT sensors monitoring system performance. The solution predicted potential failures, allowing the company to address issues before they escalated.

By leveraging real-time monitoring with integrated alerts, the maintenance team could take preventive actions, reducing equipment breakdowns and optimizing overall system performance.

Results​

With Opstree’s predictive maintenance solution, the company achieved significant reductions in operational costs and equipment failures, while optimizing resource allocation and improving customer satisfaction.

  • Reduced equipment downtime by 25%.
  • Increased energy savings by 20%.
  • Improved compliance rates by 30%.

Tools Used​

  • scikit-learn (ARIMA)
  • Amazon SageMaker
  • Time-series analysis models