Data Sience & Data Analytics

Your data. Your head start. Our AI solutions.

We make AI effective in financial services—from the initial use case concept to full-scale implementation. Together, we unlock the potential in your data, automate processes, and create reliable decision-making frameworks—efficiently, transparently, and tailored to the needs of both business units and IT.

Our services

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Making sound decisions despite growing complexity

Increasing regulatory requirements, complex end-to-end processes, and fragmented data landscapes make it difficult to make quick, secure decisions in the financial services sector. At the same time, cost and efficiency pressures, as well as expectations regarding transparency and compliance, are placing greater demands on organizations, business units, and IT.

Your path to increasing efficiency with data science and AI

By choosing findic, you can rely on solutions that take effect quickly. In many cases, short-term efficiency gains can be achieved with pre-trained models – often without having to set up your own complex specialized AI infrastructure.

If standard solutions are not sufficient, we develop customized AI models that are precisely tailored to your business, regulatory and technical needs.

Our structured approach is based on proven methods such as CRISP-ML(Q) and is characterized by …

… transparent project phases
… agile implementation processes
… high quality and traceability
… regulatory assurance for the financial industry

Why AI is crucial now?

Artificial intelligence is no longer a topic for the future – it is a key success factor for banks, insurance companies and other financial services providers. Anyone investing in data-driven processes and automation today will secure decisive competitive advantages.

We can help you to …

… identify processes with relevant AI and automation potential, …
… prioritize economically viable use cases …
… and implement tailor-made AI solutions for your business and IT domains.

The result: lower costs, higher process quality and a noticeable increase in productivity along your entire value chain.

Data & AI expertise that makes a difference

We combine deep industry and process knowledge with modern data science and AI expertise to achieve sustainable improvements. Our services range from the initial analysis to productive use:

Explorative data analysis and data preparation
Development of meaningful analysis and management dashboards
Predictive analytics and machine learning models
Development of custom AI algorithms for financial processes
Integration into existing IT landscapes

We provide you with comprehensive support – from strategy to implementation and operation.

FAQs - Data Science & Data Analytics

What data analytics challenges do you typically address in the financial services sector?

These often involve improving control and transparency: KPI frameworks, management dashboards, self-service analytics, and ad-hoc analyses for business units (e.g., sales, operations, risk). The goal is to achieve a unified view of data and decision-making—from definition through implementation.

Which data science use cases are particularly useful?

Typical use cases include forecasting, segmentation, next-best-action, anomaly and fraud detection, as well as text and document analysis (e.g., classification, extraction). We prioritize these together based on business impact, data availability, integration effort, and traceability requirements.

What data do we need—and how do you handle data quality, governance, and compliance?

We start with a data and use-case check: sources, data flows, quality criteria, and technical definitions. Building on this, we establish traceable pipelines, documentation, and quality controls so that analyses and models remain verifiable and can be operated securely in regulated environments.

How does AI differ from traditional analytics—and when is each approach worthwhile?

Analytics creates transparency and controllability (KPIs, dashboards, evaluations). AI/machine learning is worthwhile when patterns need to be identified in large data sets, forecasts need to be generated, or decisions need to be automated. Often, the best solution is a combination: robust analytics as a foundation, with AI applied where it provides additional value.

How can generative AI (e.g., LLMs) be meaningfully deployed in the financial sector?

Typical use cases include document and knowledge work (summarization, extraction, classification), support functions in customer service, and assistance with internal business processes. Key considerations here include data protection, access policies, prompt/output controls, and seamless integration into existing processes and governance frameworks.