AI redrawing Finance’s data and technology foundations

Artificial intelligence (AI) is radically reshaping the data and technology foundations of Finance, the growing business and operational benefits of AI are energising Finance leaders and sharpening their focus on data which is now widely recognised as the critical backbone required to truly unlock AI‑enabled processes.

Financial institutions in the UK and globally are moving beyond siloed systems and periodic processes toward a digital-first ecosystem where data is a strategic asset and much of the Finance workflow is automated or augmented by AI.

While many organisations have invested in automation and AI, the actual adoption is still in early stages and focuses on specific use cases. To bridge the gap between AI’s promise and practice, banks are undertaking broad changes in how they manage and govern data and technology. The following sections outline these transformational shifts, presenting a strategic view of how AI is re-defining the core pillars of Finance data and technology management and operations.

Marios Symeonidis

Director, CS&P FT

KPMG in the UK

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From monolithic lakes to agile data ecosystems

AI’s emergence has upended traditional data architecture assumptions in Finance. In recent years, firms have sought to centralise data into large warehouses or lakes, aiming for single repositories of truth. Today, that approach is giving way to more flexible, distributed ecosystems designed to meet Finance’s increasing need for agile delivery and trusted source of data. Organisations increasingly adopt architectures like data lakehouses, implement data fabric integrations between the data lakehouse and their Finance systems, and stand up a data mesh operating model focusing on data as a product. These setups allow data to remain in federated locations, but be accessed and combined on demand while also ensuring clear domain ownership.

Such structures enable scalability avoiding data movements and reconciliations between the Finance systems enabling a decentralised data management model; each business domain (e.g. Finance, Treasury, Risk) owns its data as a “product”, is responsible for its governance and provides standardised interfaces, usually through a marketplace, for others to use it.

The benefit of this approach is agility. These curated business data products are trusted, governed and can scale with demand.

Cloud technology is a big enabler here; firms are migrating core data platforms to cloud-based services, which provide a scalable backlog allowing modularisation of not only elasticity for big AI workloads but also modular components which provide increased flexibility.

An optimal data architecture can significantly impact data management costs and implementation timelines for new systems or services. Firms that build true data platforms and embrace open, modular systems achieve significantly quicker time-to-market for new insights and products while not materially impacting operating costs. In practice, this means a bank can spin up a new capital model in weeks by pulling the right customer, market, and transactional data through a fabric or mesh the first time, whereas in the past it might have taken months just to assemble the requisite data in one place with further effort on evidencing its validity.

Data quality reinvented with self-healing data

As data becomes more dispersed and immediate in Finance, ensuring its quality and reliability is paramount. Traditionally, data quality management is labour-intensive and reactive. Finance teams would clean data via manual rules or fix errors during reconciliation, far downstream from where the data was generated. Now, AI techniques are enabling additional automated preventative and detective data quality controls, in effect creating ‘self-monitoring’ and ‘self-healing’ data pipelines. AI-powered systems can identify, classify, and even remediate anomalies in near real-time. This represents a shift from static rule-based checks to dynamic anomaly detection that catches subtle issues and reducing false positives.

An agentic AI workflow could automatically correct a data format, convert currencies inconsistently recorded, or enrich data by pulling reference information from other sources. When issues are more complex, humans are bought into the loop for further investigation, focusing human effort where it’s truly needed.

By embedding such AI-driven quality checks, Finance can always be aware of the state of the data at any point in time, thus greatly reducing downstream headaches while building greater trust. Finance teams can be more confident that the numbers feeding their reports are accurate and consistent. We’re moving away from the standard “garbage in, garbage out” to a world where if data has problems, AI can substantially improve its usability.

Modernising core systems with embedded AI functionality

To truly embed AI into the Finance function, banks are overhauling their core systems such as ERP platforms, GRC and compliance systems, and regulatory and management reporting tools to be AI-powered and highly automated. This modernisation is leading to a new wave of straight-through processing in Finance, where end-to-end workflows execute with minimal human intervention. Financial processes that once required multiple human-machine hand-offs are becoming seamless and intelligent, from transaction processing to period-end close.

Modern ERP and Finance systems are increasingly coming with embedded AI capabilities and are increasing at a fast pace. These range from machine learning models to AI assistants that can answer queries like, “What were our top five expense anomalies this week?”. Gartner predict that Finance organisations using cloud-based ERPs with in-built AI assistants will be able to close their books 30% faster by 2028 than those without.

Of course, these changes come with challenges, from the need to integrate AI with legacy systems to managing the new risks introduced by automation. But the direction is set: Finance IT is moving towards systems that are not only automated but “self-driving” with humans in a supervisory role. Firms upgrading to these intelligent systems aim to achieve both efficiency and intelligence, redefining operational excellence in Finance; success will be measured not just by cost savings, but by the ability to respond rapidly to events, the ease of compliance, and the freeing of human talent for strategic activities.

Where Finance and technology workforces converge

The rise of AI in Finance is not only a story of technology and processes it is equally a story of people and cultural change. Historically, the Finance and IT functions were clearly delineated accountants and business analysts on one side, IT and data teams on the other, each with separate roles and language. Leading to age-old problems in responsibilities, ownership and funding related to Finance technology and data. Firms are recognising that fully exploiting AI depends on the next level of skills integration, where Finance, data, and technology capabilities are increasingly combined within roles themselves.

In practical terms, this means Finance teams increasingly include resources who can code, build data pipelines, or at least comfortably use advanced analytics tools, while technology teams better understand Finance business context. The silos between business and IT are breaking down in favour of integrated teams; it’s now common to see a data scientist sitting in the Finance department, or a Finance analyst embedded within a data engineering squad co-creating Finance solutions.

This convergence is driven by necessity. AI projects in Finance seldom succeed without domain input; a machine learning model needs the nuances that Finance experts provide. Likewise, those experts need to understand what the technology can (and can’t) do in order to ask the right questions and interpret the results. The effect is a shared language making the Finance function more technically adept and the tech function more business-savvy.

Parallel to this is a shift in data ownership and accountability. In traditional setups, the IT department “owned” the databases and systems, and business users were consumers. That model is fading. Modern data strategies advocate that business domains should own their data, they create it, so they are responsible for its quality and upkeep. IT’s role becomes providing platforms and guidance rather than acting as gatekeeper. This democratisation of data goes hand in hand with AI adoption. If Finance people are to use AI insights daily, they must have confidence and control over the data feeding those insights. We’re seeing job roles adapt accordingly, for example, Finance Data Owner and Data Steward roles appear in organisations to bridge gaps between technical teams and Finance leadership.

Culturally, embracing AI requires a mindset shift reminiscent of the big shifts of the past. Many firms are investing heavily in reskilling programs, encouraging Finance staff to learn data analytics or programming basics, and tech staff to learn Finance fundamentals. The concept of continuous learning is becoming ingrained, as the skill requirements will keep evolving with the technology.

Conclusion

The Finance sector stands on the cusp of a profound transformation, driven by AI and backed by the smarter use of data. The changes underway are strategic and structural. We see a reimagining of data architecture to break constraints and boost agility; a new era of data quality and governance that uses AI to ensure integrity from the ground up; core systems evolving into intelligent, autonomous platforms; and a retooling of workforce and accountability models to support a data-and-AI-centric way of working. These shifts are interlocking pieces of a larger puzzle: building a Finance function that is faster, more accurate, and more forward-looking than ever before. It is a future where the ledger closes itself, reports build themselves, anomalies correct themselves and Finance professionals focus on interpreting insights and driving strategy.

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