Tasks

• Build a machine learning model to forecast price movements across selected trading instruments.
• Develop an AI agent that interprets model outputs to inform automated decision-making processes.
• Integrate reporting functionality to generate and deliver performance metrics and KPIs.
• Implement an external trigger system (e.g., email and webhook) to automatically activate the agent and handle follow-up responses.

Challenges

Solutions

• Developed adaptive models with online learning capabilities to regularly retrain on new data and adjust to changing market trends.
• Integrated macroeconomic indicators and real-time event data into the model to improve responsiveness to market shifts.
• Used automated feature engineering (AutoFE) tools to rapidly test and refine a wide range of input variables.
• Worked closely with financial experts to vet model features and interpret market behavior accurately.
• Deployed advanced NLP pipelines to extract signals from news headlines, social sentiment, and regulatory updates.
• Hosted models on high-performance infrastructure, optimizing serving latency and throughput using FastAPI.

Outcomes

• Delivered an AI agent capable of handling real-time price predictions for multiple instruments.
• Enabled automatic activation via email or webhook, with the agent retrieving inputs, executing analysis, and sending back KPI reports.
• Reduced manual intervention in forecasting and reporting workflows.
• Created a scalable foundation for automated decision-support tools in financial operations.

Technologies Used

Tasks

• Develop a classification model to help customers understand their spending habits.
• Create an adjustable ML model to profile credit and debit transactions.
• Build a clustering ensemble to define regular and irregular transactions.

Challenges

The main challenge was to create a competitive advantage by utilizing AI to distinguish the financial app from others and to grow expertise in advanced analytics and machine learning.

Solutions

• An adjustable ML model that profiles customer transactions by merchant, purchase type, and income.
• A clustering ensemble that categorizes each user’s transactions as regular or irregular.

Outcomes

• The ML saving advisor became one of the most used features of the app.
• The first version of the model allowed accurate labeling of regular and irregular transactions in 80% of cases.

Technologies Used

Tasks

• Developed visualizations for client segments and profiles
• Predicted churn likelihood with machine learning models
• Set up data warehouse and database schema
• Automated data updates for Power BI dashboard
• Implemented alert system for data updates and exchanges
• Created tools for churn retention strategies
• Compiled detailed project documentation

Challenges

Building a robust data infrastructure and predictive models for effective churn retention.

Solutions

• Set up a data warehouse (likely AWS Redshift or Google BigQuery).
• Use scikit-learn models and gradient boosting for churn prediction.
• Automate data processes and implement retention strategies.

Outcomes

Improved client retention and data-driven decision-making through advanced analytics.

Technologies Used

Tasks

• Establish a single client-consultant touchpoint for client questionnaires.
• Enable advanced analytics for consultants.
• Generate HRPP, Salary reports, and Graded Pay Structure.

Challenges

Solutions

• Developed a secure, single-touchpoint service with dynamic dashboards and drill-down capabilities.
• Built an autonomous system with a modern, user-friendly interface.
• Used historical data for predictive analytics and personalized recommendations.

Outcomes

Improved customer satisfaction with a user-friendly service, enhanced consultants’ effectiveness, enabling greater project scalability, reduced consultants’ data tasks, freeing resources, and showcased EY’s commitment to modern solutions and brand value.

Technologies Used