Finance & Insurance

AI Solutions for Finance

Financial & Insurance Services have recently taken the lead in digital transformation, eagerly embracing the potential of Big Data, Data Engineering, AI, and the AI – Machine Learning. Those areas, used to collect, sort, process, analyze, and convert massive, complex data sets into meaningful business insights, are considered a “make or break” factor for traditional finance institutes, constantly threatened by tech-savvy FinTech start-ups. 

No wonder. The potential of AI and Data Engineering is almost limitless. Data – text, numeric, and images – can be used in numerous ways to advance the ability to recognize patterns, anticipate future events, create smart rules, make intelligent, data-based decisions and automated communication with clients. More finance-oriented usages of AI are fraud detection, high-frequency trading, risk management, and investment management, but – as we said above – with AI sky is the limit.

More finance-oriented usages of AI are fraud detection, high-frequency trading, risk management, and investment management, but – as we said above – with AI sky is (almost literally) the limit.

Customer advanced analytics

AI algorithms can conduct in-depth customer analytics, and score and segment clients depending on financial history, current situation, and forecasted economical trends. Based on data, it is easier to deliver personalized, risk-included offerings.

Image analysis

Financial institutions can speed up the verification processes with image analysis. In the finance industry, biometric data from images of customers’ faces can be linked to financial data and used to verify applications; insurance companies can reach out for similar tools to automate damage analysis processes.

Robotic Process Automation

RPA, by applying rule-based business processes, can reduce human efforts with repetitive tasks automating such as analyzing, categorizing, and evaluating documents.

Business benefits


Improved Customer Experience

In finance service – just like in any other – customers constantly seek greater convenience. They want to open bank accounts, transfer money, and change currency using their smartphones without leaving home.

With AI, Machine learning, and computer vision, it is possible. There is no need to verify paper documents, as authenticating a client’s identity can proceed with advanced techniques like voice recognition, biometrics, and image recognition.

Reduced Number of Potential Errors

Errors in the finance industry can have severe repercussions, and even the most experienced employees are not immune to making them.

AI helps reduce this risk and prevent exposing the institution to liability and causing irreparable damage.

Process Automation

​Finance institutions are typically involved in creating multiple, very detailed financial reports. Report development time can be significantly reduced by implementing analytical cubes that provide the end-user with multiple analytical options.

Also, visualization tools help navigate the data universe and analyze links between occurrences – all in a much more intuitive and user-friendly way.

Using Computer Vision for document verification

We develop Computer Vision solutions, allowing computers to understand what they “see” in the real world. Using digital images from cameras significantly reduces the risk of approval of fake documents.

Moreover, online document verification boosts the customer experience.

Using data engineering for environments

We build advanced data pipelines to collect data from many disparate silos and collect them into a single platform.

They provide financial organizations with an overall view enabling them to make intelligent business decisions across departments.

Using BI solutions to outrun the competition

We create business Intelligence solutions that automatically transform raw data into meaningful business insights that can be leveraged to strengthen competitive advantage, drive innovation, and boost the customer experience of end clients.


Manual labor
Poor customer experience
FinTech Competition

Manual workflows are typically inefficient, cause end-to-end production lags, and expose organizations to costs.

AI can automate labor-intensive processes, making them more objective, reducing business costs while improving quality.

The finance sector struggled with improving the customer experience because of the need for thorough document verifications.

With AI, ML, NLP techniques, and computer vision, customer experience can be smoothed and more personalized simultaneously.

Competition within the financial services industry is severe because many new, online-only players have entered the market, and their services are very appealing, especially to younger customers.

Moreover, consumers are less concerned with brand loyalty and identity and have no problem switching banks in exchange for convenience.


Intelligent scoring models
Recommendation engines
Fraud Detection
Trade Settlements

Calculate risk with ML algorithms

Machine learning algorithms are able to calculate risk ratio and personalize offers for clients based on their financial profile, previous behaviors, and potential risk involved.

Data-driven suggestions

With a data-driven recommendation engine that – based on transactional and customer behavior – can suggest to customers additional services, financial organizations can increase sales metrics with up-and cross-selling.

Correctly classify transactions

With AI and ML models, financial companies gain the ability to correctly classify transactions as either legit or fraudulent, based on details such as amount, merchant, location, time, and others.

ML helps set the root cause of failed trades and analyze the reasons

More advanced solutions can even predict which trades might fail in the future, enabling taking preventive action in advance.

Customer stories

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