Maisa is building AI-driven infrastructure for banking operations through Digital Workers, specialized AI agents designed to execute end-to-end lending workflows spanning document intake, validation, fraud and identity checks, underwriting logic, and exception handling. Pulse 2.0 interviewed Maisa Chief Strategy Officer Andrew Volkov, whose background includes Deutsche Bank and co-founding Workfusion, to learn more about transforming auto lending with agentic AI.
Andrew Volkov’s Background
When asked about his background in enterprise automation and financial services and how it led him to AI-driven transformation in banking operations, Volkov explained:
I’ve spent my career in enterprise automation and financial services, particularly focused on how complex operational processes in regulated industries can be made faster, more consistent, and more scalable.
Across roles in enterprise software and banking environments, including Deutsche Bank and co-founding Workfusion, I saw the same pattern repeatedly: financial institutions had strong digital front ends, but the core operational workflows underneath, especially in lending, were still heavily manual.
That gap between digital interfaces and real execution is what ultimately led me to focus on AI-driven transformation in banking operations in this new wave of agentic AI.
The Auto Lending Problem
When asked what problem in auto lending Maisa was created to solve, Volkov said:
Maisa was created around a very clear operational problem: time-to-fund in lending is still measured in days, not hours, and that delay is now a competitive disadvantage.
In auto lending specifically, every application triggers a long chain of manual steps: document checks, identity verification, fraud screening, underwriting, and deal structuring.
Each step introduces delays, rework, and inconsistency, especially when files are incomplete or flagged. The result is not just inefficiency, it’s lost deals. Dealers don’t wait, they move to the fastest funding decision.
Digital Workers For Lending
When asked what Maisa actually does inside an auto lender’s workflow, Volkov detailed:
Maisa introduces what we refer to as Digital Workers, specialized AI agents designed to execute end-to-end lending workflows.
In auto lending, this means they ingest full loan packages, including contracts, bank statements, identity documents, and payroll records, and then extract and validate structured data across all sources.
They also run fraud, identity, and income verification checks while applying lender-specific approval logic and risk scoring frameworks.
This results in clear, consistent outcomes for each application, whether classified as clean, flagged, or requiring exception handling with full explanation.
Rather than fragmented processes spread across multiple teams and systems, the workflow becomes a single, coordinated execution layer with end-to-end traceability and control.
Reducing Time-To-Fund
When asked how AI is reducing time-to-fund and improving the dealer experience, Volkov explained:
The core impact of AI here is removing sequential manual bottlenecks.
Instead of passing applications between teams and waiting for individual validations, Digital Workers can process and cross-check all required information in a single execution flow.
This reduces time-to-fund from multiple days toward hours, which directly improves dealer satisfaction because faster funding means fewer lost deals and less uncertainty in the process.
AI Adoption In Regulated Lending
When asked about the challenges of getting AI adopted in regulated lending environments, Volkov said:
The biggest challenge is not technology, it’s AI’s operational trust in regulated environments.
Lenders need systems that are explainable, auditable, and consistent under regulatory scrutiny. That means AI cannot behave like a black box.
The second challenge is integration with legacy infrastructure, where workflows were never designed for real-time, end-to-end execution.
How Maisa’s Platform Has Evolved
When asked how Maisa’s platform has evolved from early automation to its Digital Workers approach, Volkov explained:
Maisa has evolved from traditional automation approaches into a Digital Worker model.
Instead of automating individual tasks, we now orchestrate full workflows across lending operations, from document intake to fraud detection to deal structuring.
Each agent is designed for a specific operational function, but they work together as a unified execution system rather than isolated tools.
Competitive Differentiation
When asked what makes Maisa different from other AI or automation solutions in financial services, Volkov said:
What differentiates Maisa is that it is not positioned as a traditional automation tool or a layer on top of existing systems.
It is an execution system built around specialized AI agents, Digital Workers, that can perform structured, multi-step business processes with full traceability and control.
Another key factor is predictability: AI costs remain under control, outputs are designed to be consistent, auditable, and suitable for regulated decision-making environments.
The Future Of AI In Auto Lending
When asked where he sees AI having the biggest impact in auto lending over the next two to three years, Volkov explained:
Over the next few years, lending operations will move toward near real-time decisioning in many segments, particularly in auto finance where speed is a key competitive factor.
The role of humans will increasingly shift from manual processing to exception handling, oversight, and policy refinement, while execution is handled by AI systems.
AI As An Execution Layer
When asked whether there was anything important about AI in lending, fraud prevention, or funding speed that had not been covered, Volkov concluded:
One of the biggest misconceptions about AI in lending is that its value lies primarily in prediction or scoring.
In reality, the largest inefficiency in the system is execution, the ability to reliably process, validate, and coordinate complex workflows across multiple data sources and systems in real time.
That is the layer where most operational friction still exists today, and where AI is starting to have the most immediate and measurable impact.
