Invoice Automation for Banking & Financial Services

Business Challenges

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

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

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

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

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

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

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Solution Approach

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

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

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

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

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

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

Business Impact

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

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

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

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.

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

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

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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

50+

AI & ML
Engineers

40+

AI & ML
Projects for
reputed Clients

5 yrs

in AI & ML
Engineering

Awarded as Winner among 1000 contestants at TechSHack Hackathon

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