BI & Machine Learning

Machine Learning Model Migration

Machine Learning Model Migration AREA: MACHINE LEARNING, IT INFRASTRUCTURE, DEVELOPMENT SUPPORT PROJECT LENGTH: 4 MONTHS BRIEF One of Hungary's leading financial institutions decided to introduce new machine learning-based solutions at group level. The first implementation phase of which was to introduce the solutions

Implementation of Data Governance in a leading Hungarian bank

Implementation of Data Governance in a leading Hungarian bank AREA: FINANCIAL INSTITUTION SOLUTIONS PROJECT LENGTH: 12 MONTHS BRIEF Developing a Data Governance Process and Framework to develop the organizational, operational and IT frameworks of Data Governance, thus moving the critical analysis and reporting

Developing the IT background of IFRS 9 transition

Developing the IT background of IFRS 9 transition AREA: FINANCIAL INSTITUTION SOLUTIONS PROJECT LENGTH: 16 MONTHS BRIEF During the project, BCS introduced an IFRS calculating and accounting solution on bank level. Developed the input interfaces for back-end systems handling lending and treasury products.

GDPR – data removal

GDPR - data removal AREA: BANK PROJECT LENGTH: 11 MONTH BRIEF The most important aspect of GDPR (General Data Protection Regulation), to only store personal data in the time period, when having the information for some reason is absolutely necessary. When these reasons

Machine Learing based demand planning

Machine Learing based demand planning AREA: BI & MACHINE LEARNING PROJECT LENGTH: 6 MONTHS BRIEF Fuel demand prediction for an oil company with machine learning methods with self-learning capabilities. BCS was responsible for the design of relevant data interfaces and data control layer,

CRM refactoring with datawarehousing

CRM refactoring with datawarehousing AREA: BI & MACHINE LEARNING PROJECT LENGTH: 24 MONTHS BRIEF Our banking client started to establish a greenfield, unified enterprise datawarhouse to migrate all the existing datawarehouse of finance, risk, CRM and operation.BCS is responsible for the Implementation of

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