AI cuts processing time without losing control
Risk review, customer service, and document processing are the functions where financial services firms lose the most time. We build agents that respect control obligations.
AI levers at financial services firms
Financial services firms operate under regulation that demands traceability and control, and under competitive pressure that demands speed. Application processing, document review, and customer questions on status and terms tie up staff in processes that consist mostly of gathering information, not the actual decision. Every delay there costs throughput time, and ultimately margin in the competition for customers.
We helped shape the technical architecture for a digital lending platform and know where structured data exists in a financial architecture and where it doesn't. AI applies where documents, forms, and customer inquiries need to be structured before a decision is made: agents extract details from documents, check them for completeness, and prepare the actual risk decision. The decision itself stays where it belongs by regulation. The result is shorter processing time with unchanged control.
Functions with the largest lever
- 1
Risk review & application processing
Agents extract and check application data for completeness before a case handler decides
- 2
Customer service
An agent answers status questions on applications and terms directly from the case system
- 3
Marketing & content
Product and terms materials come from current data sheets instead of manual upkeep
- 4
Finance & reporting
Automated preparation of metrics for internal and regulatory reporting
- 5
Software development
Architecture and Agentic Engineering for lending platforms and application flows, from our own project experience
→ Throughput time
Typical use cases
Document extraction for applications
An agent reads proof of income, ID documents, and forms and checks them for completeness before routing them onward.
Status agent for customers
Answers questions on an application's processing status directly from the case system, without a query to a case handler.
Preparing the risk decision
Summarizes reviewed application data in structured form; the decision itself stays with the responsible team.
Terms and product materials
Automated updates to product sheets when terms change, instead of manual upkeep.
Architecture for application flows
Technical advisory and implementation for digital lending platforms and similar regulated application processes.
Prerequisites
| Area | Requirement | Typical state |
|---|---|---|
| Data | Application and case data in one system, not spread across email and paper | Varies; older case management systems make structured access harder |
| Systems | Case system and document management with API access, in line with regulatory requirements | Usually in place; access rights and logging need to be explicitly defined for agents |
| Organization | A risk or compliance owner who sets the boundaries of the agent's role | Should already exist; the role for AI-assisted pre-review is usually not yet defined |
Requirement: Application and case data in one system, not spread across email and paper
Typical state: Varies; older case management systems make structured access harder
Requirement: Case system and document management with API access, in line with regulatory requirements
Typical state: Usually in place; access rights and logging need to be explicitly defined for agents
Requirement: A risk or compliance owner who sets the boundaries of the agent's role
Typical state: Should already exist; the role for AI-assisted pre-review is usually not yet defined
Our approach at financial services firms
We start by separating which steps in the application or service process are pure information gathering and which represent a regulatorily protected decision. We automate only the first category with an agent, with full logging. The second category stays with people, working from better-prepared data. We fix this split in writing before we build.
| Ausgangslage | Umsetzung | Ergebnis | |
|---|---|---|---|
| Digital consultancy, Hamburg | Digital consultancy needed external architecture expertise for three parallel client projects. | Tech DD of an e-commerce architecture, greenfield CMS concept, architecture for a credit platform. | The consultancy could continue all three client projects on a reliable technical foundation. |
| Real estate platform, Berlin | Real estate platform in Berlin, technical risk unclear ahead of a decision. | Tech DD report with a risk picture and a target software architecture. | The decision rested on a documented technical foundation. |
| EdTech company, engineering organization | Engineering team used AI coding tools individually, without a shared workflow. | Toolchain, adapted workflow, and quality gates for AI-assisted development. | The team works agentically as the default, with controls that fit the new way of working. |
Ausgangslage
Digital consultancy needed external architecture expertise for three parallel client projects.
Umsetzung
Tech DD of an e-commerce architecture, greenfield CMS concept, architecture for a credit platform.
Ergebnis
The consultancy could continue all three client projects on a reliable technical foundation.
Ausgangslage
Real estate platform in Berlin, technical risk unclear ahead of a decision.
Umsetzung
Tech DD report with a risk picture and a target software architecture.
Ergebnis
The decision rested on a documented technical foundation.
EdTech company, engineering organization
Ausgangslage
Engineering team used AI coding tools individually, without a shared workflow.
Umsetzung
Toolchain, adapted workflow, and quality gates for AI-assisted development.
Ergebnis
The team works agentically as the default, with controls that fit the new way of working.
Frequently asked questions
Initial call: 30 minutes, concrete.
We look at your application and service processes and tell you where an agent saves time without costing control.