AS corporations expand, rapid revenue growth often exposes a critical bottleneck: legacy back-office financial systems. For Canon India, a leading provider of imaging technologies and solutions, aggressive market penetration placed acute pressure across its financial operations on the subcontinent. Identifying each incoming customer payment and matching it to the right open invoice relied on labour-intensive manual work, turning routine accounts receivable into a significant operational friction point. The finance team at Canon India found themselves occupied in repetitive administrative tasks, limiting their capacity for higher-value strategic initiatives.
Rather than applying a quick technical patch, Canon India partnered with Deutsche Bank to execute a comprehensive overhaul of its end-to-end collections ecosystem. By embedding machine learning directly into its enterprise resource planning (ERP) environment, the company converted an error-prone manual workflow into an automated success story.
High friction and inefficiency
To appreciate the full scope of the transformation, one must examine the operational friction that preceded it. Canon India receives payments from a vast network of enterprise customers, and each payment must be matched against specific open invoices. The finance department faced a dense and fragmented web of incoming payment and remittance data.
The traditional workflow for identifying customer payments was tediously slow. Finance personnel had to manually analyze incoming bank statements line by line, mapping each payment to its corresponding customer accounting code in a process that was time-consuming and highly prone to human error. In an early attempt to boost operational efficiency, Canon India deployed a legacy accounting tool from a partner bank. However, this initial automated solution did not fully meet the company’s expectations and business requirements.
Matching incoming funds to specific open invoices introduced further operational complexity. Customer payment advices arrived through unstructured, disparate channels ranging from PDFs and Excel spreadsheets to standard email bodies. Reconciling these fragmented documents against open ledgers required heavy manual intervention.
Engineering an impactful solution
Recognizing that legacy workarounds were unsustainable, Canon India's executive leadership demanded a fundamental shift in how financial operations were conducted. To replace these fragmented workflows, Canon India partnered with Deutsche Bank to deploy AlgoriQ, a cash application and reconciliation solution powered by machine learning algorithms trained on millions of dataset records. Integrated directly into Canon’s ERP system, the tool was engineered to match incoming funds against specific open invoices. It extracts remittance data across unstructured channels and executes continuous three-way matching across bank statements, payment advices and open ledgers.
With the technological framework established, the executive vision quickly shifted from administrative necessity to strategic value. Takeshi Iwasawa, CFO & Director – Finance & Accounts at Canon India, notes that the platform's impact extended far beyond traditional cost-cutting metrics. “Overall, the value we have gained from AlgoriQ goes far beyond cost savings,” Iwasawa explains, noting that the AI-driven transformation drastically improved productivity by eliminating manual effort, accelerated cash application and established unprecedented visibility across the entire collections cycle.
Crucially, this operational shift redefined the role of the finance department itself. “Instead of spending time on repetitive manual tasks, our teams can now focus on more value-added tasks,” Iwasawa emphasizes, attributing the breakthrough to “Deutsche Bank's strong partnership collaborations with our team”.
Achieving the CFO's vision required prioritizing process design before software deployment. Rachel Whelan, APAC & MEA Head of Corporate Cash Management & Global Head of Payments & Transactional FX Product Management at Deutsche Bank, notes that the project began by addressing core data extraction challenges across enterprise clients generating large transaction volumes. “When we talked to Canon India initially, it was really about a step change that they wanted, not just incremental benefits. They wanted to get an uplift across their end-to-end processes,” Whelan recalls.
Joint teams from Deutsche Bank and Canon India invested significant time in whiteboarding operational workflows and mapping how information moved across existing ERP platforms and legacy systems. This disciplined diagnostic phase addressed a fundamental reality of enterprise automation. Artificial intelligence is only as effective as the quality and reliability of the data it relies on.
Through rigorous analysis, the teams identified core problem statements, uncovered data gaps and inconsistencies, and systematically cleansed and validated underlying data inputs. This groundwork created a trusted data foundation, enabling the subsequent deployment of machine learning with greater confidence, accuracy and scalability.
Understanding how it works
Operating inside Canon’s ERP system, AlgoriQ executes an automated three-way reconciliation by matching payment advice, bank statement entries, and outstanding ledger records. Beyond simple matching, the tool autonomously completes complex accounting activities with minimal human intervention and manages sub-general ledger entries.
Rajeev Vashisht, Assistant Director – Credit Control at Canon India, points to operational speed and precision as the tool's defining advantages. “The key differentiator has been the speed and accuracy with which the output file is generated by this tool and how the accounting happens,” he observes.
Vashisht explains that the system supports the complete cash application lifecycle through a dynamic buyers directory where customers are identified via bank account details and specific parameters. Onboarded customers are automatically integrated into this directory for seamless recognition during payment runs. The engine automatically extracts and interprets relevant information using artificial intelligence and machine learning, actively monitoring open items through to final resolution.
From transformation to value
The operational dividends materialized rapidly for Canon India. Within the first quarter of deployment, the solution achieved a 90% straight-through processing (STP) rate, ultimately climbing to approximately 97% overall process efficiency.
Beyond internal metrics, faster and more accurate payment processing enhanced the overall customer experience, giving Canon India greater control and transparency over working capital. For Deutsche Bank, the project established a new benchmark for corporate cash management. Whelan highlights her pride in delivering a solution that created a lasting business capability rather than a simple process tweak, reflecting that while the 97% STP is a striking headline metric, the true achievement lies in liberating the finance team at Canon India from manual exception handling to support enterprise growth.
The collaboration between Canon India and Deutsche Bank earned industry acclaim, receiving the award for Best Payments and Collections Solution in India at The Asset’s Triple A Treasurise Awards 2026.
Looking ahead, both organizations remain focused on driving further innovation through clean data analytics, developing additional solutions to enhance cash visibility and unlock deeper analytical insights from payment and remittance data. The Canon India experience offers a clear blueprint for modern corporate treasury: when advanced AI technology is paired with deep process understanding and clean data inputs, routine administrative functions can be converted into resilient, growth-oriented business capabilities.
