Finance

Integrating Real Time Data Processing and Machine Learning for an Investment Management Firm

Focus Areas

Machine Learning (ML)

Digital Transformation

Data Processing

Business Problem

A high-frequency trading and investment management firm was struggling to process and analyze large volumes of financial data in real time. The company worked with slow and inefficient manual processes for validating transactions and forecasting financial trends. This limited the company’s ability to react quickly to market changes, make critical decisions, allocate reources efficiently, and make the most of investment opportunities. 

Key challenges:

  • Poor Latency: The existing systems of the company were not optimized for processing large volumes of data in real time. This delayed transaction validation and reporting, and was especially difficult for the quick decision making required for high-frequency trading.

  • Lack of Automation: The company faced issues such as slow workflows and human error for reporting and financial forecasting. This limited the ability to react quickly to market shifts. 

  • Limited Forecasting Ability: The company could not quickly and accurately predict market trends to adjust investment strategies in real time with manual processes. This made it difficult to capitalize on emerging opportunities and limited their competitive edge.

  • Scalability: The company’s infrastructure couldn’t scale with the increasing volume of high-frequency financial data. This impacted the performance and operating expenses since resources would be inefficiently allocated.

The Approach

Through close collaboration with the client’s internal IT, data science, and operations teams, Curate’s consultants designed and implemented a real time data processing system that would reduce latency, improve transaction validation, and enhance financial forecasting. The solution would be powered by AWS services and machine learning and leverage scalable cloud infrastructure and automated ML models.

Key components of the solution:

  1. Discovery and requirements gathering: Our consultants carried out a thorough assessment of the client’s existing data processing and validation systems (including detailed interviews with internal stakeholders, an analysis of the current infrastructure’s performance bottlenecks, and an evaluation of the institution’s data workflow). Through this, the following key objectives were identified:

    • Reduce latency and speed up real time data processing to enable quicker decision-making.

    • Automate transaction validation to reduce human error and eliminate manual operations.

    • Use machine learning models to predict financial trends and optimize investment strategies.

    • Ensure the infrastructure can grow with the increase in data volume with 99.7% uptime.

  2. Designing a real time data processing system: The team selected AWS services technologies such as Kinesis for event streaming and Apache Flink for stream processing to build a comprehensive and real time data processing system. These technologies had the ability to handle high-frequency data streams efficiently and at scale.

    • Event Streaming: Curate’s consultants designed the stream processing pipeline to handle large volumes of data with low latency, ensuring that data could be processed in milliseconds. Kinesis allowed the client to take in and process financial data streams in real time, capturing every market transaction as it happened. 

    • Stream Processing: Apache Flink was integrated into the architecture to process and analyze data in real time. This allowed the client to perform real time computations on the data streams such as transaction validation, anomaly detection, and financial reporting. Flink significantly reduced the time required for data validation and reporting with its ability to process data as it came in.

    • Scalable Storage: Online Analytical Processing (OLAP) databases allowed for efficient querying and analysis of large datasets and enabled storage and retrieval of historical data for reporting and compliance purposes.

  3. Machine Learning Integration for Financial Forecasting: The team integrated machine learning models that could analyze real time market conditions and make predictive forecasts.

    • Machine Learning Models: Machine learning models were developed and deployed to predict market trends based on real time data using Python and AWS SageMake. These models were trained on historical financial data and made use of various algorithms to forecast potential price movements, which would help the client to make data-driven decisions.

    • Automated strategy adjustments: Through integration of these models with the real time data processing pipeline, the system could adjust the client’s investment strategies based on real time market insights automatically. For instance, on detecting a potential rise in a specific stock or asset class, the system could adjust the trading strategies automatically to get maximum returns. 

  4. Automation of transaction validation: The company faced challenges due to its reliance on manual processes for transaction validation. To address this, Curate automated the process within the real time data processing pipeline.

    • Automated rule-based validation: A rules-based engine was implemented within Apache Flink to automatically validate transactions as they went through the system. This validation process ensured that company met the compliance and regulatory requirements for all financial transactions without manual intervention.

    • Real time reporting: The automation of the transaction validation system provided real time reporting and compliance monitoring on the transactions, which reduced the risk of errors and delays.

  5. Infrastructure scalability and reliability: Next, a scalable infrastructure using AWS Elastic Compute Cloud (EC2) and AWS Auto Scaling was implemented to handle increasing volumes of data without compromising performance.

    • Adjusting to real time demand: Auto Scaling policies were configured to dynamically adjust compute resources based on real time activity.

    • High availability and Uptime: A redundant, fault-tolerant architecture was implemented to ensure the system maintained 99.7% uptime. Even in the event of a hardware or network failure, the system could stay continuously available because of multiple availability zones and automated failover mechanisms.

  6. Continuous Monitoring and Optimization: AWS CloudWatch and AWS X-Ray were set up to continuously monitor system performance and identify areas for further improvement.

    • Real time Monitoring: AWS CloudWatch provided real time insights into system performance metrics, including data processing latency, transaction validation times, and machine learning model accuracy. The client could monitor the health of the system and ensure that it was performing at peak efficiency.

    • Ongoing Optimization: Our team provided continuous support including regular system audits and performance reviews. This helped identify emerging bottlenecks to optimize the data processing pipeline and ensured that the system continued to meet the client’s performance and scalability requirements.

Business Outcomes

The transition from Waterfall to Agile, led by Curate Consulting, resulted in transformative improvements for the healthcare provider:

Reduced Latency


The client was able to validate transactions and react to market developments almost instantly, which reduced data processing latency by 35%.

Enhanced Uptime


The system's redundant, fault-tolerant architecture increased the uptime to 99.7%, which guaranteed constant availability for financial reporting and real time trading.

Improved Financial Forecasting


The company was able to make better investment plans and decisions due to a 15% increase in the accuracy of its financial forecasts.

Scalable Infrastructure


The AWS-based infrastructure allowed the business to expand with ease and steadily as data quantities increased.

Enhanced Transaction Efficiency


By reducing human error by 40% and increasing overall efficiency, automated transaction validation freed up staff members to concentrate on other high-priority duties.

Sample KPIs

Here’s a quick summary of the kinds of KPI’s and goals teams were working towards**:

Metric Before After Improvement
Data processing latency 500 ms 325 ms 35% reduction
System uptime 97.5% 99.7% 99.7% achieved
Manual transaction validation errors 100 errors/month 60 errors/month 40% reduction
Financial forecasting accuracy 70% 85% 15% improvement
Transaction processing capacity 10,000 transactions/hour 15,000 transactions/hour 50% increase
**Disclaimer: The set of KPI’s are for illustration only and do not reference any specific client data or actual results – they have been modified and anonymized to protect confidentiality and avoid disclosing client data.

Customer Value

Curate Consulting’s expertise in Agile methodologies not only improved operational efficiency but also enhanced the healthcare provider’s ability to serve their patients more effectively:

Increased time-to-market


The CI/CD pipeline improved the capacity to promptly provide new financial services to customers by cutting the time needed to implement new features by 40%.

Improved uptime and reliability


Customers received improved service thanks to a 35% reduction in downtime which was brought on by deployment problems and configuration errors.

Conclusion

Curate’s collaboration with the company successfully increased the accuracy of financial projections, increased uptime, and decreased latency. By deploying AWS-based infrastructure and machine learning, the company’s system was able to effectively manage increasing data volumes. Curate’s expertise in delivering high-performance, real time data solutions allowed the company to respond swiftly to changes in the market in real time.

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