Smart Finance: Harnessing AI and ML for Document Digitization with OCR and NLP Technology






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About the Client

Our client was an American financial company with retail and commercial banking branches in the USA. The client’s company serves both individual consumers and small and medium-sized businesses or large enterprises, while actively implementing Artificial Intelligence technologies.

Business Context

The task that the client set for our Data Scientists was to obtain certain information from PDF scans of documents. It was necessary to extract metadata related to certain information about the company’s customers, accounting data and other securities of the financial company.

Amazinum Data Engineers In Action

Data Scientists of Amazinum used OCR technology to extract both printed and handwritten text from the image. The technology involves the electronic or mechanical conversion of text images into machine-coded text.

After transferring scans into a format that was convenient to work with ( text ), specialists used NLP tools to recognize and extract from the text required fields and words that met the specified metrics. NLTK, and SpaCy were used for this.

Challenges Faced

The problem with this task was the resolution of the images. Because each photo/image corresponded to a different resolution, the OCR model did not handle handwritten text well. To solve this problem, Data Scientists at Amazinum pre-processed the data, including converting the image to black and white spectrum (grayscale). This ensured the clarity of the image and the possibility of further work.

Outcome of Implementing Artificial Intelligence Technology in Financial Services

Enhanced Efficiency

Data entry, fraud detection, and customer support are just a few of the manual tasks that ML and AI can automate to save a tonne of time and money.

Personalized recommendations

Personalized recommendations and AI-powered chatbots can offer a more seamless and customized customer experience, which can increase satisfaction and retention rates.

Risk management

By using machine learning (ML) algorithms to examine massive datasets and find trends and abnormalities, financial institutions can more accurately identify and reduce risks.

Fraud Detection

By examining transaction patterns, AI can identify fraudulent activity in real time and prevent financial losses for both clients and institutions.

Predictive analytics

ML algorithms can forecast future market trends, consumer behavior, and investment opportunities by analyzing historical data. This capability enables businesses to make more informed decisions.

Compliance and Regulatory Reporting

By automating reporting procedures and guaranteeing accurate and timely submissions, AI can assist financial institutions in adhering to regulatory requirements.

Cost Reduction

ML and AI can assist financial institutions in cutting costs related to human labor and errors by automating repetitive tasks and enhancing operational efficiency.

Improved Investment Management

By analyzing market data and offering insights to optimize investment strategies, AI-powered algorithms can improve investor returns.

Real-Time Decision Making

Financial institutions can respond quickly and intelligently to changes in the market or client needs because machine learning algorithms can process data in real time.


Vitaliy Fedorovych

CEO, Data Scientist at Amazinum

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