Top 10 Technologies for Finance Digital Transformation ['25]

88% of finance executives report increased AI adoption post-pandemic, with technologies like Robotic Process Automation (RPA) saving thousands of hours annually.¹

We present which financial processes can be automated, and what tools are suitable for digital transformation based on 10 case studies and our experience researching and publishing 100+ articles on automation, AI and Cybersecurity.

Automation Technologies

1. RPA 

RPA are bots programmed to undertake repetitive tasks. For example, RPA in finance is used to reduce the workload of accounting teams, by automating numerous tasks, such as journal entries or intercompany reconciliations, and only requiring a final review from them. RPA is proved to save finance departments 25,000 hours of work annually.  ²

Their other benefit is their consistency. For periodically repetitive procedures, such as journal entries during financial close, these bots can be scheduled to work as commanded (i.e. recording sales data on journals as they come in), thus minimizing the possibility of missed deadlines.

Case Study

Bank of America (BoA) – BoA launched a large-scale RPA program to streamline repetitive tasks in banking operations. The bank deployed software “bots” for processes like mortgage servicing, payments, and compliance checks. This automation layer sits on top of legacy systems and mimics human keystrokes to perform routine data entry and validation.

• Impact: RPA eliminated manual touchpoints – for example, the bank cut average handling time per case from 20 minutes to 4 minutes in certain loan servicing workflows. Error rates dropped dramatically (some defect rates fell to zero), customer service call times shrank by 10–15%, and operational costs fell due to fewer manual fixes. The initiative delivered cost efficiencies, increased productivity, and reduced risk in multiple units, hitting 95% of its targeted benefits under budget.

2. Workload automation 

Another technology used in financial transformation is workload automation. Workload automation software schedules and initiates workflows automatically, thus requiring minimal human intervention.

Workload automation use cases tend to focus on backend processes like file transfers. For example, financial organizations may need to transfer thousands of files on a daily basis to track information about their branch operations. The primary user of workload automation tends to be IT. Enterprises that require complex workflows often refer to enterprise job schedulers.

Case Study

Arla Foods – The international dairy cooperative Arla overhauled its financial closing process using workload automation (Redwood’s Record-to-Report platform). After several mergers, Arla’s finance team needed to standardize and schedule tasks like balance-sheet reconciliations, intercompany postings, and month-end adjustments across 60 sites. They implemented an automated closing orchestration that triggers and runs nearly all closing activities without human intervention.

• Impact: Automation reduced the time to close the books by 30% (from 10 days to 7), exceeding the initial goal​. It harmonized 100% of process and data quality across business units and automated 98% of close-related processes, freeing staff for higher-value work. In one case, a reconciliation task that took four hours was cut to 30 minutes. Overall, employee effort on closing activities dropped by up to 50%, data accuracy improved, and real-time monitoring of the close became possible across the enterprise.

3. Document automation tools

Document automation is a technology for creating electronic documents, such as invoice issuance for processes in accounts payable, accounting, and tax filings. The software can gather sales data such as, order quantity, price, customer information, and other usable data from underlying ERP systems such as order management software or CRM systems to automatically, and accurately, create an invoice in real-time based on the data it can create thanks to OCR technology.

Case Study

Indecomm – Indecomm, a mortgage technology provider, leveraged document automation to speed up loan processing. The company’s IDX (Intelligent Document Extraction) system uses machine learning (via Amazon Textract) to classify loan documents and extract key data for underwriting. This goes beyond basic OCR by validating data and even enriching it from multiple pages or forms, automating what was previously a labor-intensive review of mortgage packets.

• Impact: Data classification and extraction that used to take hours now completes in five to seven minutes, on average. Lenders using Indecomm’s IDX have been able to cut underwriting and origination time in half by removing manual data entry and comparison tasks.

4. Orchestration

Orchestration is a tool that allows for the automatic coordination of different financial ERP systems found in the infrastructure of a modern enterprise. Much like an orchestra conductor, orchestration automates the sequential undertaking of processes.

In financial close, for instance, there is an eight-step process in the checklist that should be carried out before the close is achieved. Automated solutions leverage orchestration tools to allow all these different tasks in the checklist, such as balance reconciliation and intercompany accounting, to be in progress.

Case Study

Delen Private Bank – Belgium-based Delen Private Bank modernized its core banking processes with process orchestration technology. The bank was relying on aging COBOL systems and manual workflows that varied by branch, which made it hard to adapt to new regulations and scale operations. By adopting the Camunda platform, Delen redesigned several key workflows (such as securities settlements) into end-to-end automated processes with business-rule engines and human-in-the-loop steps where needed.

• Impact: Delen transformed time-consuming, paper-based tasks into efficient digital workflows. For example, its trade settlement process – which required staff to print and cross-check transaction statuses – is now largely automated, with a dashboard showing real-time status and only exceptions handled by people. Overall, process orchestration improved the bank’s agility and resource use: it became easier to adjust workflows to meet new regulatory changes (e.g. switching to T+1 settlement cycles), automated processes freed employees for “smarter” tasks, and all branches now follow uniform, optimized workflows, enhancing control and reducing errors.

AI-Powered Solutions

5.AI

Artificial intelligence covers a variety of technologies that can be used to enhance financial tasks, such as credit extension.

For example, in the order-to-cash (O2C) process, once an item is added to the cart, retailers have to assume that the customer has the money to pay for the good, so that they can set the logistical wheels in motion. That “assumption” is basically extending the customer a line of credit (LC) until it comes to payment processing.

Financial solutions leverage AI to evaluate applicants’ credit history more quickly, efficiently, and accurately. There are pay-as-you-go credit agencies that can do that for you. So the automated financial solution and the credit agency collaborate with each other to gather credit score on one end, and send it to your ERP on the other, thus allowing the wheels of the O2C process to be set in motion.

Case Study

Mogo – The Canadian fintech Mogo applied AI to enhance its compliance processes. It replaced an outdated keyword-based system with an AI-powered screening platform (Minerva) to identify politically exposed persons (PEPs) in financial transactions. This machine learning solution adapts quickly to changing regulations and cross-matches entities with greater accuracy.

• Impact: After implementing the AI model, Mogo identified 5% more PEPs than a global solution (and 40% more than a local Canadian solution), improving risk coverage. The platform reconciled PEP data across clients, boosting compliance confidence and reducing manual effort.

6. NLP & reporting automation bots

NLP is a sub-field of AI that gives computers the ability to understand and interpret human language. NLP has many use cases in finance but is arguably most efficient and effective for extracting large volumes of data across different silos and giving it to RPA bots that are specialized in reporting. These bots, then, will generate periodic financial reports, such as P&L statements, tax reports, and other financial statements.

The benefit of NLP being used complementary to reporting bots is that generating reports will be automated and shorter to complete. In addition, because financial reports are meant to educate high-level executives and investors on the financial state of the company, it will mean that they are provided with the most accurate sets of data.

Case Study

PMorgan Chase – The bank’s business intelligence team implemented an NLP-driven chatbot to simplify financial data analysis for executives. This natural language understanding (NLU) solution lets users query complex financial datasets using plain English questions. Instead of writing SQL or digging through dashboards, managers can ask, for example, “What were our Q4 expenses by region?” and get an instant answer. The chatbot uses JPMorgan’s internal data and understands context to retrieve accurate results.

• Impact: JPMorgan reports that it cut the time spent on data analysis by 40%, as executives and analysts no longer need to manually compile reports or code queries for routine questions. This speeds up decision-making and reduces the load on BI teams for ad-hoc reports. It also lessens the chance of misinterpretation, since the bot delivers standardized answers straight from the data source.

7. Process mining 

Process mining is an analytical tool that helps businesses understand and analyze the speed and efficiency of their everyday processes by detecting patterns in process event logs. It’s through process mining that businesses would identify the bottlenecks in their financial department that could be remedied with automation.

For example, in the purchase-to-pay process, it’s claimed that sourcing, procurement, and accounts payable have their own specific department, with each optimizing their functionality subjectively. Businesses can leverage process mining software to get a more accurate estimate of how long each purchase-to-pay cycle takes.

• Learn more about the use cases of process mining in finance.
• Discover how to use process mining in p2p and order-to-cash processes.

Case Study

Tech Data – Global IT distributor Tech Data employed process mining to optimize its procure-to-pay (P2P) operations. By using Celonis to analyze event logs from its ERP, Tech Data gained a clear view of how purchase orders, invoices, and payments were actually flowing – including all the reworks and bottlenecks in the process. This transparency allowed the company to pinpoint inefficiencies like approval delays and pricing errors, and then automate or fix those parts of the workflow.

• Impact: It achieved a 57% reduction in total cycle time – meaning purchases to payments now flow in less than half the time they used to​. By discovering duplicate or needless steps, the company also automated a large portion of invoice handling. Tech Data reached a 95% automated invoice processing rate, vastly reducing manual invoice matching and data entry​. These changes led to faster payments (capturing more early payment discounts) and fewer payment errors.

Security & Infrastructure

8. Blockchain 

Blockchain is a distributed ledger for recording all decentralized transactions.

In finance, it can be used to improve the transparency and security of data, thus reducing the possibility of fraudulent tampering. For instance, a company’s financial documents and statements can all be secured on blockchain. This lowers the feasibility of any employee or executive to make changes to the data on the sheets once they are on the chain because they become instantly verifiable by auditors.

In a survey by Duke University ³ , 78% of high-level executives exhibited a preference to tamper with quarterly reporting in order to soothe stakeholders’ sentiment. Blockchain will eliminate that possibility because all adjustments are recorded and verified in real-time, not just at the end of quarters.

Case Study

HSBC – Global bank HSBC built an internal blockchain platform called FX Everywhere to streamline foreign exchange flows. This distributed ledger system records and nets internal FX transactions across the bank’s multiple balance sheets​. By using a shared, permissioned ledger, HSBC automated many manual reconciliation steps in its intra-company payments and settlements.

• Impact: Over one year, HSBC’s blockchain handled more than 3 million FX transactions (about 150,000 payments totaling $250 billion) among its global branches​. HSBC gained a consolidated view of cash flows and cut out delays – an example of blockchain reducing errors and costs in back-office financial operations​. The success has HSBC exploring offering the tool to multinational corporate clients for their cross-border treasury operations.

9. Cloud computing

Cloud computing is the practice of having data storage spaces without on-premise IT infrastructure. This technology is useful for most back office automation and functionalities do not require on-premise infrastructure. All that responsibility is passed on to the developers. Moreover, cloud functionality makes for 24/7 remote and online accessibility by all authorized personnel. This will mean that the progress of financial tasks, be it close to O2C or S2P, can all be remotely monitored independent of location.

Data privacy is another benefit that cloud computing offers financial services. Gartner claims that through 2020, public cloud infrastructure as a service suffered 60% fewer security incidents than those in traditional data centers.⁴ So, as they argue, “security should no longer be considered a primary inhibitor to the adoption of public cloud services.”

Security of financial data is of the utmost importance, and leveraging cloud solutions can offer enhanced protection of data and documents.

Case Study

Capital One – U.S. bank Capital One embraced a cloud-first strategy to modernize its IT and financial operations. Partnering with AWS, Capital One migrated key customer account systems and internal apps to the cloud in a systematic, phased approach. This move was aimed at improving agility, scalability, and cost efficiency. High-impact, customer-facing applications (like mobile banking and account management) were prioritized in the migration to maximize benefits.

• Impact: The cloud’s elasticity allows Capital One to scale computing resources on demand, ensuring performance during peak loads without overspending during lulls. This scalability and the use of cloud services also enabled more advanced analytics (like real-time fraud detection and personalized services), improving the customer experience and risk management.

Data Extraction & Investment Tools

Web scrapers

Lastly, automation tools are not only for back-end financial procedures. They can also be used for investment. Let’s say a company wants to invest in a business or even a financial automation product. They can use web scrapers to scrape the internet for public reviews, prices, and capabilities of the product. Alternatively, they could scrape publicly available financial documents and other factors of interest regarding the investment project that they are interested in.

• Learn more about using web scrapers for investment.

Case Study

Northwestern Mutual – The insurance and financial services firm Northwestern Mutual uses web scraping to gather real-time market and competitor intelligence. A team of analysts automatically collects data from competitor websites, social media, and industry forums to track offerings and consumer sentiment​ This includes scraping competitors’ product pages and press releases to stay alerted on any new insurance or annuity products entering the market.

• Impact: Web scraping provides Northwestern Mutual with up-to-the-minute insights that inform its strategy. For example, the company’s scraping tools identified a rival’s new product launch within days of its release. This early warning enabled Northwestern Mutual to quickly adjust its own product marketing and communicate with clients about comparable offerings. The scraped data also feeds into pricing and promotion decisions – monitoring competitors’ rates and deals in an automated way helps Northwestern Mutual’s finance and product teams make timely adjustments.

Further Reading

If you are curious to learn more about the technologies being used in the finance sector, read:

• Real-Time Asset Valuation With Asset Accounting Automation
• Top 8 Chatbot Use Cases & Applications in Finance
• AI Credit Scoring Models: In-depth Guide

If you would like to leverage a FinTech solution for your business, we have prepared a data-driven list of vendors.

And if you believe your business would benefit from a digital transformation software, we have a data-driven lists of digital transformation consultants.

External Links

• 1. The postpandemic workforce: Responses to a McKinsey global survey of 800 executives | McKinsey. McKinsey & Company
• 2. Robotic Process Automation Saving Finance Work Hours | Gartner.
• 3. Value Destruction and Financial Reporting Decisions: Financial Analysts Journal: Vol 62, No 6.
• 4. Gartner “Cloud Strategy Leadership”