Banking Data Analytics & Real-Time Fraud Detection

Banking data analytics, real-time fraud detection, and regulated AI for financial institutions.

Banks and financial institutions operate under unique constraints: strict regulation and supervision, complex legacy core banking systems, high expectations for correctness and reliability, and limited tolerance for experimentation in production. Modern banking analytics and real-time fraud detection require streaming data platforms that can process millions of transactions per second while enforcing compliance at every layer — this is the reality of banking big data today.

Acosom works with banks and financial institutions to design, build, and operate streaming data platforms, business-critical software, fraud detection systems, and AI-enabled solutions — while fully respecting regulatory, security, and operational requirements.

We have hands-on experience with large Swiss banks regulated by FINMA, credit banks in Liechtenstein, international banks, fintech companies operating across Europe, on-prem and hybrid environments, and core banking–centric data landscapes.

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What Financial Institutions Gain

When working with partners who understand banking’s unique constraints.

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Systems Aligned with Regulation

Platforms and software designed with FINMA expectations, auditability, and traceability built in from the start.

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Secure, Controlled AI Adoption

AI and LLMs deployed on-premises or in private environments, ensuring data sovereignty and regulatory compliance.

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Clean Integration with Core Banking

Solutions that integrate cleanly with existing core banking platforms without compromising stability or data integrity.

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Explainable, Auditable Systems

All decisions and data flows are traceable, reviewable, and explainable — meeting internal audit requirements.

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Long-Term Maintainability

Systems built to be operated and maintained by internal teams, not creating permanent external dependencies.

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Balanced Innovation and Control

Ability to adopt modern technology while respecting correctness, reliability, and compliance requirements.

Banking Success

From Fragmented Core Banking Data to Governed Data Products

A large Swiss bank needed to transform raw core banking data from Avaloq into usable, governed data products for downstream teams. Data was complex, domain models were difficult to navigate, and teams struggled to access trusted information without impacting core system stability. We worked within the core data product delivery team to ingest and map core banking data, simplify complex domain models, aggregate raw data into meaningful representations, define stable data product interfaces, and serve data products via streaming for internal consumers. We also designed search capabilities across very large booking volumes enabling fast, reliable querying with consistent semantics.

Result: Downstream teams could investigate transactions efficiently, build additional internal services, and rely on trusted, well-defined data — all while protecting core system stability. The bank gained governed, self-service access to critical banking data without compromising auditability or performance.

Discuss Your Banking Needs

Understanding the Reality of Banking IT

In banking, success is not about adopting the newest technology fastest.

It is about correctness over speed, traceability over convenience, reliability over feature richness, and compliance over experimentation.

Our work reflects this reality.

We design systems that integrate cleanly with existing core banking platforms, respect data ownership and lineage, are auditable and explainable, and can be operated and maintained by internal teams.

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What We Do for Banks & Financial Institutions

Our banking engagements typically focus on three tightly connected areas.

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Core Banking Data & Platforms

Governed data products, search across large booking volumes, integration with Avaloq and other core banking systems, streaming data delivery, and traceability.

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Business-Facing Software for Operations

Credit processing workflows, document handling, clerk-facing applications, integration with existing banking systems, and operational tooling.

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Regulated, Secure Use of AI

On-premises and private LLM infrastructure, AI-assisted document analysis, fraud detection, decision support with human oversight, and explainable AI outputs.

Banking Experience in Practice

Real-world examples from our work with financial institutions.

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Governed Data Products & Search Across Large Booking Volumes

For a large Swiss bank, we transformed raw core banking data from Avaloq into usable, governed data products. We simplified complex domain models, aggregated raw data into meaningful representations, and designed search capabilities across very large booking volumes. This enabled downstream teams to investigate transactions efficiently, build internal services, and rely on trusted data — all while protecting core system stability.

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AI-Enabled Credit Processing & Document Fraud Detection

For a credit bank in Liechtenstein, we designed and implemented end-to-end credit processing tools used directly by clerks. The solution included digital workflows for credit applications, structured handling of applicant data, and AI-assisted document analysis for fraud detection. AI compared applicant names and attributes across documents, detected inconsistencies, and flagged suspicious submissions. Crucially, AI supported clerks — it did not make autonomous decisions, and final decisions remained with human users.

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On-Prem AI & Private LLM Infrastructure

Using LLMs within banks required addressing security, data protection, and regulatory constraints upfront. We designed and implemented on-premises and private LLM infrastructure ensuring sensitive financial data never leaves controlled environments, models are isolated, and access is strictly auditable. This enabled institutions to use LLMs safely in internal workflows, apply AI to document analysis and decision support, and avoid risks associated with shared external AI services.

Working with International Banks & Fintechs

Beyond Switzerland and Liechtenstein, we support international banks, US-based financial institutions, and fintech companies operating across Europe.

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Balancing Speed and Control

The challenge is enabling fast iteration without breaking compliance, allowing team autonomy without losing governance, and using AI without creating data or regulatory risk. We help organizations design architectures that scale across regions, enforce policies without slowing teams down, and use AI as decision support rather than uncontrolled automation.

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Data Sovereignty & Multi-Region Considerations

For global banks and fintechs, data often needs to be processed in specific regions, accessed by teams in different jurisdictions, and governed under multiple regulatory regimes. We design systems that respect regional data boundaries, enforce access policies at runtime, support hybrid and multi-region deployments, and remain auditable and explainable.

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Security, Compliance & Regulation

Our banking work is consistently aligned with FINMA expectations, internal risk and compliance processes, auditability and traceability requirements, strict access control and separation of duties, and on-prem, hybrid, and controlled cloud deployment models. We are comfortable collaborating with compliance teams, risk management, internal audit, and security departments.

How Our Services Fit Banking & Fintech

Depending on the institution, we support different aspects of your technology journey.

Consulting: Platform strategy, data product design, governance models

Engineering: Implementation of platforms, business software, and AI solutions

Managed Services: Reliable operation under defined support and availability models

Training & Enablement: Enabling internal teams to own and evolve systems safely

Within banks and fintechs, we often work with CIO/CDO organizations, data and integration teams, core banking and downstream system teams, credit and operations units, and architecture, security, and governance functions.

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Frequently Asked Questions

What is real-time fraud detection?

Real-time fraud detection is the continuous evaluation of transaction, login, and behavioural events — within milliseconds of occurring — against fraud and AML patterns so suspicious activity is blocked, challenged, or flagged before the transaction completes. Unlike end-of-day batch fraud analytics, real-time fraud detection operates on a streaming data platform and feeds decisions directly into authorisation, payments, and customer-communication systems.

A production real-time fraud detection system typically combines:

  • Event-streaming backbone: Apache Kafka as a durable, low-latency transport for transactions, card authorisations, payment events, and login signals
  • Stream processing: Apache Flink for stateful evaluation — sliding windows, sequence patterns, per-customer state, velocity checks, and device / IP correlation
  • Feature store / real-time features: Continuously updated customer, card, device, and account features available at decision time
  • Rules + ML models: Deterministic rules for regulatory AML checks, supervised models for card/payment fraud, and anomaly detection for novel patterns
  • Decisioning layer: Sub-second scoring, policy evaluation, and action (approve, deny, step-up, hold, queue for review)
  • Case management and human-in-the-loop: Queueing, escalation, investigator workflows, and feedback loops that retrain models
  • Governance and audit: Full audit trail of inputs, features, model versions, scores, and decisions — aligned with FINMA, BaFin, PCI DSS, and DORA

Acosom builds real-time fraud detection on open streaming technologies (Apache Kafka, Apache Flink, ClickHouse) integrated with bank core systems and private LLMs for case support — strictly inside the compliance perimeter and with decisions auditable end-to-end.

What is banking data analytics?

Banking data analytics is the practice of using the full range of data a bank produces — transactions, card activity, payments, customer interactions, risk signals, core-banking events, and operational telemetry — to support decisions across the institution. It is broader than classical reporting: modern banking data analytics combines historical analytics with real-time event processing, so decisions reflect what is happening now, not only what happened yesterday.

Typical banking data analytics capabilities include:

  • Real-time risk and fraud analytics: Pattern detection on payments, card, and transaction streams for AML, fraud, and sanctions
  • Customer analytics: Unified customer views across products, with lifetime-value, retention, and next-best-action models
  • Operational analytics: Live KPIs for payments, clearing, settlement, and core-banking processes
  • Regulatory reporting and compliance analytics: Automated, auditable pipelines aligned with FINMA, BaFin, Solvency II, GDPR, PCI DSS, and DORA
  • Actuarial and credit-risk analytics: Reserving, PD/LGD modelling, and stress-testing on governed data products
  • AI-assisted analytics: Decision support, document automation, and internal knowledge retrieval via private LLMs — strictly inside the compliance perimeter

Acosom builds banking data analytics platforms on streaming-first architectures — Apache Kafka for event ingestion, Apache Flink for stateful stream processing, ClickHouse and lakehouse formats for historical analytics — with governance, lineage, and access controls enforced at runtime, not only in catalogs.

What is banking analytics and how does banking big data work in practice?

Banking analytics is the use of transactional, behavioural, risk, and operational data to support decisions in banking — from real-time fraud detection to regulatory reporting and customer analytics. Banking big data extends this by combining historical datasets with high-throughput live event streams (payments, card transactions, trading events, device telemetry) into a single governed platform.

A modern banking analytics / banking big data platform typically includes:

  • Event-streaming ingestion: Apache Kafka for payment events, card authorisations, trading messages, and CDC from core banking systems
  • Stream processing: Apache Flink for real-time enrichment, feature computation, and fraud/AML pattern detection on live transactions
  • Analytical storage: Lakehouse formats (Iceberg, Paimon) and columnar engines (ClickHouse) for sub-second queries over billions of events
  • Governance & compliance: Runtime enforcement of access policies, lineage across data products, and auditable pipelines aligned with FINMA, BaFin, GDPR, PCI DSS, and DORA
  • AI & private LLMs: Decision support, document automation, and risk models running on on-prem infrastructure — never exposing customer data externally

Acosom builds banking analytics platforms that operate under strict regulatory constraints — real-time signals where they matter (fraud, risk, operations), governed data products across the bank, and private AI strictly inside the compliance perimeter.

How do you handle FINMA and regulatory requirements?

Regulatory compliance is built into our approach from the beginning, not added later.

Our approach:

  • Systems are designed with auditability and traceability from the start
  • Data flows are documented and explainable
  • Access controls and separation of duties are enforced at runtime
  • AI outputs are reviewable and explainable
  • We collaborate directly with compliance, risk, and audit teams

We have hands-on experience working with large Swiss banks regulated by FINMA and understand what is expected in practice.

Can you integrate with our core banking system?

Yes. We have extensive experience integrating with core banking platforms, particularly Avaloq.

Our approach:

  • Clean integration that respects core system stability
  • Proper handling of complex domain models
  • Data lineage and traceability maintained
  • Safe decoupling for downstream consumers
  • No compromise to data integrity or audit requirements

We understand that core banking systems are not experimental playgrounds — integrations must be reliable, correct, and maintainable.

How do you use AI in regulated banking environments?

We use AI as decision support and assistance, not as autonomous decision-making.

Our approach:

  • AI is deployed on-premises or in private environments
  • Sensitive data never leaves controlled environments
  • Models are isolated and auditable
  • Outputs are explainable and reviewable by humans
  • Final decisions remain with authorized users
  • Usage aligns with regulatory expectations

This enables practical AI adoption while maintaining data sovereignty, compliance, and trust.

Do you only work with large banks?

No. We work with:

  • Large Swiss banks regulated by FINMA
  • Credit banks in Liechtenstein
  • International banks
  • Fintech companies across Europe
  • US-based financial institutions

The size of the institution matters less than the criticality and regulatory constraints of the systems involved.

What if our systems are mostly on-premises?

That’s not a problem. Many of our banking clients operate in on-prem or hybrid environments.

We have extensive experience with:

  • On-premises data platforms
  • Hybrid cloud and on-prem architectures
  • Private AI and LLM infrastructure
  • Air-gapped or highly restricted environments
  • Integration with legacy core banking systems

We adapt to your operational and regulatory reality, not the other way around.

Can you help us migrate from legacy systems?

Yes, but we approach migrations carefully.

Our approach:

  • Understand the existing system’s role and criticality
  • Define clear migration scope and risk boundaries
  • Migrate in controlled, verifiable stages
  • Maintain parallel operation where necessary
  • Ensure auditability and rollback capability
  • Never compromise operational stability

In banking, migrations are not experiments — they must be planned, tested, and executed with full awareness of business and regulatory impact.

Ready to evolve your banking platforms safely, securely, and sustainably? Let’s talk about your specific challenges.

Discuss Your Banking Needs
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