Data foundations

We build solid data foundations that help financial institutions organize information about customers, products, and risk, introduce quality controls, and prepare the organization for analytics, AI, and regulatory reporting.

Regulatory compliance
Faster deployment of AI solutions
Lower system integration costs
Consistent customer view and data quality control

CHALLENGES

Why does transformation start with data?

Customers expect fast, personalized services. Fintechs are accelerating the pace of innovation, while regulations are increasing expectations for banks and insurers.

As a result, strong data foundations are no longer optional — they are a necessity.

Fragmented information
Customer data is spread across core systems, policy systems, CRM platforms, digital channels, and risk systems. Without a single customer view, personalization and decision-making become difficult.
Regulatory pressure
GDPR, DORA, and supervisory requirements demand full transparency of data and decisions. Institutions must know where data comes from and how it has been used.
Untapped potential
Banks and insurers collect vast amounts of information about customers and transactions, but without a consistent architecture it is difficult to use it effectively in analytics and business decisions.
AI blocked by data
Before AI models reach production, teams often spend months integrating and preparing data. Without common standards, it is difficult to move from pilot to deployment.

OUR APPROACH

Data and AI maturity model

Each level shows how to move from organizing data to using it in business decisions, analytics, and AI.

AI opportunity mapping
Processes such as sales, underwriting, risk assessment, and fraud detection are analyzed to identify where data and AI can shorten decision times, reduce losses, or increase revenue.

Prioritized use cases
Prioritized list of AI use cases with the highest business potential is defined, along with the order of implementation to enable the first measurable results quickly.

Data architecture and AI
Data architecture, models, and integrations required to support selected use cases are defined to ensure AI operates within real business processes.

Data integration
Information from core or policy systems, claims systems, CRM, and digital channels is integrated into a single ecosystem, making it faster to access and easier to use in business processes.

Data quality
Data quality rules, error monitoring, and rapid correction mechanisms are introduced to ensure information is complete, up to date, and ready to support business decisions and AI.

Single customer view
Consistent customer view is created by combining information from multiple products and channels, enabling better understanding of customer needs and more personalized offers.

Data governance
Clear rules for managing information are established, along with the ability to trace its origin and use. This makes it easier to meet regulatory requirements and pass audits.

Real-time decision data
Current data, such as transactions or customer events, is used to enable immediate decisions, for example in fraud detection or during customer interactions.

Customer intelligence platform
Customer information from multiple systems is combined and transformed into practical insights and recommendations that support sales, service, and personalization.

Operational dashboards
Clear dashboards present key metrics and operational issues in near real time, supporting faster decisions and more effective process management.

AI-assisted decisions
AI models support business decisions while maintaining control and regulatory compliance. Decisions remain explainable, monitored, and auditable.

BENEFITS

What does the business gain from strong data foundations?

Business can make informed decisions about which data and AI initiatives to pursue and which to postpone. Shared project evaluation criteria — based on business value, risk, and feasibility — make it easier to set priorities and reduce conflicts between teams.
Operational and management decisions rely on the same information across the organization. This reduces the risk of incorrect decisions and shortens the time needed to prepare data for analytics and AI projects.
Decision models can incorporate external data, such as market or behavioral signals. This improves the accuracy of scoring and segmentation and enables faster responses to market shifts and changing customer behavior.

CASE STUDIES

Selected client projects

See all case studies

A Qatar-based global investment fund struggled with fragmented data sources that slowed decision-making. A modern…
Integrating data from key systems – ERP, HR, customer portal and core operational platforms –…
Inconsistent data governance was hindering a financial institution’s efficiency and security. A thorough audit and…

See all case studies

MARKET

Data determines the success of AI

85%

of financial institutions plan to increase their Data & AI budgets in 2025–2026

 (Gartner)

70%

of AI projects fail due to problems with data quality and availability

(McKinsey)

3–5×

faster AI deployment in organizations with mature data architectures

(Forrester)

IMPLEMENTATION

From data strategy to production AI solutions

Within 2–3 weeks, a strategic workshop is conducted to assess the current state of data, identify business objectives, and highlight areas with the greatest potential.

PoV model

What do you gain with a Proof of Value?

Measurable ROI 
Impact is demonstrated on a specific use case before committing to full implementation.
Lower risk
Technical and organizational approach can be validated before scaling.
Fast start
First results are visible within 2–3 months from the start of the project.

FAQ

Data and AI foundations FAQ

No. Organizations can start from the level that matches its current data maturity and business priorities. If a basic data strategy already exists, transformation can begin at a higher level. Discovery workshops help define the most effective starting point.

Conversation with an expert
Short consultation to understand your business goals and current data-related challenges.
Initial recommendation
We present possible directions and highlight areas where data foundations can deliver the greatest value.
Next steps
We outline how a Proof of Value or the first implementation stage could look.