Finance | dtlr







Many organizations often struggle to effectively consolidate data from disparate operational systems, and Financial Institutions are no different. To make sound decisions, better address customer expectations and stay ahead in the competition, having single version of truth across the organization is paramount. 


When Financial Organizations struggle to effectively consolidate data, it results in inefficiencies in planning, budgeting, and reporting, as well as lengthy and error-prone financial consolidation. External stakeholders including investors, customers, and governments have high expectations regarding financial disclosures -- and inadequate risk management and non-compliance can result in severe challenges and penalties. 


To achieve operational efficiency and data accuracy, many successful organizations are transforming into Intelligent Enterprise by adopting new technologies in data analytics.

Partner with us to co-create a data-driven customer and member experience, build data models and innovative professional services that are highly optimized for your business needs.


Some of our solutions:

  • Marketing & Sales Analytics:

    • Customer/member segmentation, profiling and recommendations

    • Cross-sell, up-sell modeling

    • Digital banking customer/member acquisition modelling

    • Social media analytics

    • Customer/member retention

  • Financial Analytics

    • Revenue & cost management

    • Value of products & services

  • Human Resource Management Analytics

    • Employee management & retention

  • Information Management Services:

    • Data warehouse, data mart and data lake design and development

    • Data profiling, data masking, data management

    • Information governance

    • Streaming data processing 

    • Structural & unstructured data management

  • Enterprise Analytics Services 

    • Enterprise Business Intelligence

    • Self Service BI

    • Data & text mining, predictive analytics, advanced algorithm development (e.g. machine learning)

    • Streaming data analytics

The demand is forecasted accommodating seasonality, trends, campaigns and calendar effects. Forecasts are built into suggested order quantities and disclosed to the managers in the field, for feedback and inputs.


System runs with 10 different Time-Series based algorithms such as;

  Regression Models,

  Exponential Smoothing,





We incorporated Ensemble Learning techniques in our data-driven demand forecasting model, thus the system combines the strongest aspects of the algorithms into a single method. This increases the accuracy of the forecasts since each algorithm can be sensitive to certain circumstances, while the ensemble approach tests the results of the algorithms, weighs the impact and calibrates the effect.