Financial Services
AI for financial services and asset management
Reconciliation, reporting and investment data, automated inside your own controls. Working systems in weeks for asset managers and investment firms, measured in hours and errors removed.
Free diagnostic, three minutes. No follow-up unless you ask.
What AI for financial services means
AI for financial services means using machine learning, generative AI and data engineering to take manual effort and error out of the work that runs an investment business: reconciliations, fee calculations, portfolio reporting and the data that feeds investment decisions. In asset management it usually starts in the middle and back office, where the processes repeat daily, the rules are written down and a mistake is expensive.
Our financial services work is measured in hours and errors removed. A daily reconciliation automated for a leading asset manager cut manual processing by 80 per cent. A 40-day ESG portfolio review became a one-day automated workflow with interactive dashboards. Investment-data processing fell from five days to 1.5. Performance-fee calculations ran 30 per cent faster with 15 per cent fewer discrepancies.
We are an AI consultancy of around 70 strategists, data scientists and engineers, building AI and analytics systems since 2016. We build inside your controls rather than around them, and you own the code and the IP.
Built for asset managers and investment firms
For the operations, finance, data and investment teams inside an investment business: the four problems they ask us to start on.
Operations and consolidation
Excel consolidations and manual data handling that eat analyst time. Python pipelines with embedded data-quality checks replaced one financial services firm's manual Excel consolidation, saving time and reducing errors, and an ESG-focused asset manager's 40-day portfolio review became a one-day automated workflow.
Reconciliation and fee calculation
Processes that repeat every day and have to be right. We automated a leading asset manager's daily reconciliation with an 80 per cent reduction in manual processing, and cut another asset manager's performance-fee calculation time by 30 per cent and its discrepancies by 15 per cent.
Investment research and data
Investment teams waiting on data they cannot trust. Python pipelines with data-quality checks cut an asset manager's end-to-end investment-data processing from five days to 1.5, and a seconded data team delivered proofs of concept on a global equities investment desk.
Client and management reporting
Reports built from one source of truth rather than a chain of spreadsheets. Embedded consultants automated an ESG-focused investment manager's reporting and cut errors by 90 per cent; an automated pipeline cut a global asset manager's data refreshes from hours to minutes.
What we have built for financial services
Specific engagements, specific results. Anonymised by agreement where the client asked for it.
Streamlining Excel-based workflows with Python automation
An investment management firm
How QuantSpark modernised an investment management firm's Excel-based reporting with Python automation, cutting manual effort and improving data quality without replacing familiar tools.
Automating Daily Reconciliation: 80% Reduction in Manual Processing for Asset Manager
Leading UK Asset Manager
QuantSpark automated a daily reconciliation process for a leading asset manager, achieving an 80% reduction in manual processing and faster daily trading.
Automating a global asset manager's data pipeline to streamline decisions
A global ESG-focused asset manager
Replacing a sprawling legacy spreadsheet with an automated data pipeline cut refresh times from hours to minutes and freed analysts to focus on investment advice.
Global Asset Manager Boosts Investment Decisions with Data-Driven Insights
Global Asset Manager
QuantSpark partnered with a global asset manager to streamline investment workflows by integrating fragmented data sources and enabling rapid prototyping.
Deploying advanced data engineering to accelerate investment data processing
An asset management firm
How QuantSpark cut an asset manager's end-to-end investment-data processing time by 70%, giving portfolio managers faster access to the data behind their decisions.
Automating an ESG asset manager's annual portfolio investment review
An ESG-focused asset manager
An ESG-focused asset manager cut its annual portfolio investment review from 40 days to one through an automated data pipeline and interactive dashboards.
Accelerating data-driven decision-making with embedded analytics consultants
An ESG-focused investment manager
How embedded, secondment-style analytics consultants helped an ESG-focused investment manager deploy advanced analytics fast, without the cost or commitment of permanent hires.
Automating performance fee calculations for accuracy and efficiency
An asset management firm
How automating performance-fee calculations helped an asset manager cut calculation time and discrepancies while improving revenue forecasting.
Embedding a seconded data team on an equities investment desk
A global equities investment team
A global equities investment team gained advanced analytics capability through a seconded QuantSpark data team, delivering proofs of concept and automating its yearly portfolio review.
How we work with asset managers
Most investment firms ask us to start in operations, where the payback is quickest, and move towards the investment process once the data can be trusted.
Start in operations
Reconciliation, fee calculation and spreadsheet consolidation are where AI pays back first: the steps repeat, the rules are known and the cost of an error is visible. Python pipelines with embedded data-quality checks replaced one financial services firm's manual Excel consolidation, saving time and reducing errors.
Embed rather than hand over
Some firms want capability, not a project. We have embedded secondment-style consultants with an ESG-focused investment manager, cutting reporting errors by 90 per cent, and a seconded data engineering and analytics team on a global equities investment desk, delivering proofs of concept through agile delivery.
Then the investment process
With clean, timely data, research and decision support follow. We helped a global asset manager integrate fragmented data and prototype quickly, improving how its investment decisions are made, and replaced another's legacy spreadsheet with an automated pipeline that refreshes in minutes.
Working inside your regulatory frame
Financial services firms carry obligations other sectors do not: FCA supervision, personal accountability under the Senior Managers and Certification Regime, and client data that has to stay where it is governed. We do not ask your compliance team to take AI on trust.
Accountability you can map
Under SM&CR a named senior manager answers for the systems in their area. Every agent we deploy that can act gets a named owner, a scoped role, a registry entry and an audit trail of what it proposed, which policy applied and what it was allowed to do.
Data that stays where it is governed
Your data stays in your systems by default. We deploy inside your own tenant, so client and portfolio data remains in the environment your compliance team already oversees.
Control outside the model
Prompt injection is the top LLM risk in the OWASP list, and a model cannot police itself. We use least-privilege identity, tool allowlists and a policy layer in front of any privileged action, and we choose models on fit, cost and risk rather than on a partnership agreement.
Proof, not promises
Less manual processing on a leading asset manager's daily reconciliation, through automation
Figures are drawn from completed QuantSpark engagements. Clients are anonymised by agreement; on a call we will walk you through how each number was measured and, where the client has agreed, put you in touch with a reference.
One ladder, each step earned
Every engagement starts small and earns the next step. Book a discovery call and we will tell you where to start, honestly.
Readiness Diagnostic
Free. Ten questions, a scored readiness band and an honest diagnosis. No follow-up unless you ask.
- Scored readiness band
- Plain-English diagnosis
- Recommended next step
AiRE Diagnostic
A short list of AI bets ranked by value, feasibility and risk, a recommended roadmap, and an honest read: roll out, build, buy, or wait.
- Ranked AI bets
- Value and feasibility scoring
- Recommended roadmap
- Honest build / buy / wait call
First Agent
A production-candidate agent, live on your own stack in five weeks, built with your team so you leave owning both the agent and the ability to build the next one.
- One core workflow mapped (week 1)
- One production-candidate agent on your stack
- Your people building alongside us
- Runbook, eval harness, code and IP, all yours
AiRE Rollout
Fractional AI implementation across your existing stack. From AI experiments to AI operations.
- Tool deployment on your stack
- Team enablement
- Governance and guardrails
- Monthly reviews and Slack support
Labs Discovery
A working prototype on your data, in your workflow. Not a slide deck. Validated value and a scale roadmap.
- Process and data mapping
- Working prototype
- Validated value case
- Scale roadmap
Labs Build
An enterprise-grade system embedded in your operating model. You own the code and the IP.
- Production build
- Integration and embedding
- Team handover
- 12 months support
Every project is scoped individually. Book a discovery call and we will provide a detailed proposal within 48 hours.
Financial services AI questions, answered
How is AI used in financial services?
How is AI used in asset management?
Can AI automate reconciliation?
How do you work within FCA rules and SM&CR?
Will our data leave our environment?
How do we start?
Talk to an AI consultancy for financial services that has done this before.
Start with the honest read
Three minutes, ten questions, no follow-up unless you ask. Or talk to a human first.