Invoice Automation for Banking & Financial Services

Business Challenges

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Time-consuming: Manual invoice processing takes a lot of time and effort, leading to slow turnaround time and reduced efficiency.

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High Cost: Manual processing is labor-intensive and can result in increased expenses and reduced profitability.

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Accuracy and Consistency: Manual processing is prone to human error, which can result in mistakes and inconsistencies in the data.

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Scalability: As the business grows, manual invoice processing becomes increasingly difficult to manage, leading to inefficiencies and bottlenecks.

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Compliance Issues: Manual processing can result in non-compliance with regulatory requirements and can expose the company to legal and financial risks.

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Lack of data visibility: Manual processing does not provide real-time data visibility, making it difficult to track and manage invoices.

Solution Approach

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We worked with a leading FinTech firm in Europe to automate their invoice process and fetch key information without human intervention.

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The RPA solution helped the client save 50% of the time and effort spent on manual documentation and improved overall efficiency.

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Invoices, which are unstructured data with varying templates, were converted into properly formatted text.

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Custom data was prepared, such as Invoice Number and Invoice Date, to map similar fields into the same category.

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A NER model was built to extract entities present in the invoices.

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A web UI was developed where invoices could be uploaded and returned as key-value pairs of specific information.

Business Impact

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Increased efficiency: Automating the invoice process with AI reduces manual labor and eliminates errors, leading to increased efficiency and accuracy.

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Time savings: AI can process invoices faster than manual methods, allowing finance teams to save time and focus on more strategic tasks.

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Cost savings: Automating the invoice process with AI can reduce costs associated with manual labor, such as salaries and benefits.

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Increased productivity: AI can process large volumes of invoices in a fraction of the time it takes to manually process them, freeing up valuable time and resources for other tasks.

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Better decision-making: With accurate and timely information, financial teams can make better informed decisions and take advantage of new opportunities.

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Improved customer satisfaction: Automated invoice processing can improve the speed and accuracy of invoice processing, leading to improved customer satisfaction.

Technology Stack

Trusted and Proven Engagement Model

  • A nondisclosure agreement (NDA) is signed to not disclose any sensitive information revealed over the course of doing business together.
  • Our NDA-driven process is established to keep clients’ data and IP safe and secure.
  • The solution discovery phase is all about knowing your target audience, writing down requirements, and creating a full scope for the project.
  • This helps clarify the goals, and limitations, and deliver quality products & services.
  • Our engagement model defines the project size, project development plan, duration, concept, POC etc.
  • Based on these scenarios, clients may agree to a particular engagement model (Fixed Bid, T&M, Dedicated Team).
  • The SOW document shall list details on project requirements, project management tools, tech stacks, deliverables, milestones, timelines, team size, hourly/monthly rate cards, billable hours and invoice details.
  • On signing the SOW, an official project kick-off meeting shall be initiated.
  • Our implementation approach, ecosystem, tools, solutions modelling, sprint plan, etc. shall be discussed during this meeting.

Our Award-Winning Team

A seasoned AI & ML team of young, dynamic and curious minds recognized with global awards for making significant impact on making human lives better

Awarded Bronze Trophy at CII National competition on Digitization, Robotics & Automation (DRA) – Industry 4.0

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50+

AI & ML
Engineers

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40+

AI & ML
Projects for
reputed Clients

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5 yrs

in AI & ML
Engineering

Awarded as Winner among 1000 contestants at TechSHack Hackathon

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