Financial Data Analytics

Enable seamless banking experience with data management, regulatory compliance and cloud transformation.
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Enhance customer experience and operational efficiency with data and AI

A robust data fabric can help financial institutions with real-time, accurate, and actionable insights to offer personalization, be compliant to regulations as well as grow their business and create a smarter organization. Sigmoid helps global banks and financial services companies unlock new opportunities at every stage of the customer lifecycle and explore avenues for data monetization. Our end-to-end, data engineering and advanced prescriptive analytics for financial services enable process optimization, manage risk, prevent fraud, and ensure regulatory compliance. Our BFSI data experts can help build robust data foundations and predictive ML models to increase your profitability.

improvement in risk assessment icon

3 days to 1 hour

improvement in risk assessment time for F100 investment bank
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60%

improvement in dashboard performance for a leading Fintech company
reduction in false alerts icon

65%

reduction in false alerts for a top tier investment bank

Deploy financial and banking data analytics capabilities and enable a data-driven organization

  • Centralized data repository from siloed sources
  • Data transformation and harmonization
  • Data governance and cataloging
  • Highly available and fault tolerant architecture
Blog

Check out our blog on real-time data warehousing with Apache Spark and Delta Lake for financial institutions.

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Banking & Financial Data Analytics Services
  • Marketing attribution and campaign optimization
  • Real-time decisions on lead buying and servicing
  • 1:1 personalized email marketing
  • CLTV optimization
Case Study

Explore how our ML solution for lead buying delivered an 80% precision improvement for a life insurance brokerage firm.

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ML-based approach to lead buying
  • Faster and secure data processing pipelines
  • Financial fraud analytics
  • Real-time monitoring and anomaly detection
  • Optimize costs and reduce time to compliance
Case Study

Find out how we automated risk scoring to reduce risk assessment time from 3 days to 1 hour, improving financial crimes compliance.

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Analyze compliance data
  • Data and analytics roadmap on the cloud
  • Migration from on-premise and legacy systems
  • Seamless hybrid and multi-cloud adoption
  • Optimize existing cloud performance and costs
Case Study

Explore how Sigmoid’s data migration best practices for Snowflake delivered 10x faster insights for leading Fintech company.

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Multi-cloud adoption & migration

Data Management

  • Centralized data repository from siloed sources
  • Data transformation and harmonization
  • Data governance and cataloging
  • Highly available and fault tolerant architecture
Case Study

See how we automated daily data ingestion from 100+ vendors to drive real-time analytics and BI reporting.

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Customer Experiences

  • Marketing attribution and campaign optimization
  • Real-time decisions on lead buying and servicing
  • 1:1 personalized email marketing
  • CLTV optimization
Case Study

Explore how we delivered a 7% sales lift through personalized messaging for the 12 MN+ customer base of a leading enterprise.

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Regulatory Compliance

  • Faster and secure data processing pipelines
  • Manage and analyze compliance data
  • Real-time monitoring and anomaly detection
  • Optimize costs and reduce time to compliance
Case Study

Find out how we automated risk scoring to reduce risk assessment time from 3 days to 1 hour, improving financial crimes compliance.

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Cloud Transformation

  • Data and analytics roadmap on the cloud
  • Migration from on-premise and legacy systems
  • Seamless hybrid and multi-cloud adoption
  • Optimize existing cloud performance and costs
Case Study

Know how we helped a client save $2.5 MN annually by migrating on - prem systems with 150 Mn+ rows of data to a cloud platform.

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Ready to enhance your risk and compliance strategy? Identify and manage financial risks using our enhanced financial data analytics solutions for risk management, ensuring real-time monitoring, anomaly detection and cost efficiency.

Stay ahead with risk & compliance analytics
Mitigate risks and ensure regulatory compliance with our data and analytics services designed specifically for BFSI.
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Why Choose Sigmoid?

Establish a centralized data repository

from previously siloed sources, ensuring data is easily accessible and usable

Improve marketing attribution

and campaign optimization to maximize ROI for financial services

Gain competitive edge in the marketplace

with our robust data governance and cataloging practices to maintain data quality and compliance.

Financial analytics success stories

Transform data challenges with our data engineering services

Data Pipelines

Integrate siloed data sources and get access to insights faster. Modernize your data infrastructure with high performance LCNC data pipelines.

Explore data pipelines

ML Engineering

Maximize ROI with faster deployment of ML models into production to lower the cost of model development, training and maintenance in an efficient manner.

Explore ML engineering

Cloud Transformation

Process and analyze large volumes of data with end-to-end cloud data management services. Seamlessly migrate data and analytics workloads to hybrid, multi-cloud environments.

Explore cloud transformation

DataOps

Automate data pipelines, reduce downtime, lower operational costs, and ensure reliable delivery of high-quality data to the people who need it most.

Explore dataOps

Insights and perspectives

Blog Cloud data security

How to enhance cloud data security for financial institutions

Pov Financial Crime Detection and Prevention

Sigmoid’s POV on financial crimes - detection and prevention

Guidebook Data management with DataOps

Modernizing enterprise data management with DataOps

Want to be customer-centric, compliant, and yet competitive?

Find out how F500 banks and financial institutions engage with Sigmoid to deliver the perfect banking experience!

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FAQs

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Financial analytics is critical to modern risk management strategies employed by banks and financial services, enabling them to comprehensively assess and mitigate potential financial risks. The use of data and advanced analytical techniques deliver valuable insights to facilitate risks of fraud identification, measurement, and mitigation. It aids in the detection of fraudulent activities and ensures compliance with regulatory requirements. Through stress testing and scenario analysis, organizations can evaluate their resilience under adverse conditions and create investment portfolios that balance risk and return.

Yes, organizations can utilize financial analytics to develop personalized financial products by analyzing vast amounts of financial data and gaining insights into customer behavior, preferences, and risk profiles. A data-driven approach allows for the customization of financial products to meet individual needs, such as tailored investment portfolios, personalized insurance coverage, or customized loan offerings. Leveraging analytics in the banking industry is empowering organizations to offer more targeted and relevant solutions, enhancing customer satisfaction and driving better financial outcomes.

Finance analytics streamlines account planning and revenue potential by enabling businesses to make data-driven decisions. Expert financial data analysts can help businesses identify profitable customer segments, optimize pricing strategies, and forecast revenue projections accurately. Financial analytics also helps in monitoring key performance indicators (KPIs) such as customer acquisition costs, lifetime value, and churn rates, allowing businesses to proactively address issues and improve revenue streams. By leveraging these insights, businesses can develop targeted account plans, allocate resources effectively, and maximize revenue potential.

Financial analytics offers a diverse set of tools and methodologies that provide valuable insights into a company's financial performance and potential. Here are some key types of financial and banking predictive analytics, each serving a specific purpose:

  1. Predictive Sales Analytics: This involves leveraging historical data and statistical techniques like correlation analysis to forecast a company's future sales. By doing so, it helps organizations proactively respond to market trends and changing customer behaviors.
  2. Client Profitability Analytics: This type of analytics allows companies to differentiate between clients who significantly contribute to their profitability and those who may not. It aids in making informed decisions about resource allocation and tailoring services to high-value clients.
  3. Product Profitability Analytics: Instead of assessing overall company profitability, product profitability analytics focuses on evaluating the profitability of individual products or product lines. This business intelligence insight helps businesses make targeted decisions related to product development, pricing, and marketing.

ROI can be measured through various key performance indicators (KPIs) such as improved customer acquisition and retention rates, reduced fraud losses, better risk management, and overall increased efficiency and profitability.

Increase productivity by over 30% with DataOps!

Enjoy a smoother transition to the cloud and enable better digital transformation strategies with our proven DataOps services.

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