AI-Driven Banking

Unlock the potential of data and Artificial Intelligence in Banking

We are developing banking based on data and trust

Comprehensive support for the Banking sector

We support the banking sector in the full scope of data transformation – from acquiring and integrating diverse information sources, through automating operational processes, to implementing advanced analytical solutions.

We harness the potential of Artificial Intelligence – including generative models – to build scalable, intelligent systems that support key business areas. We deliver solutions enabling event prediction, anomaly detection, and natural language analysis, thereby strengthening both innovation and security in modern banking.

Our expertise enables financial institutions to increase operational efficiency, improve risk management, and make data-driven decisions.

Modern banking powered by AI

Intelligence that drives modern banking

Implementing AI-based solutions is not just about innovation – it’s a strategic step toward flexibility, scalability, and digital resilience in today’s demanding financial world.

Artificial Intelligence is transforming the way financial institutions design, deliver, and enhance their services. Through machine learning, predictive analytics, and natural language processing, banks can automate key processes – from credit risk assessment and fraud detection to providing intelligent customer service.

A woman in a light blue shirt is sitting at a desk with a laptop, smiling and talking to another person seen from behind; in the background, there is a brick wall and a man in a suit working at another desk.

In what areas does AI support banks and companies in the financial sector?

Data

…

Traditional financial data

Valuation data

Pricing data

Volume data

Risk sensitivity data

Macroeconomic variable data

Customer interactions

Customer behavior

Marketing opportunities for cross-selling

Alternative non-financial solutions

Output data from commercial sensors

People/area observation

Geolocation data

Social media data

Automation

…

Human–user interaction

Visualization, “virtualization” of conversations, screens, PDFs, etc.

Work planning

Receiving, identifying, classifying, and prioritizing work items

Orchestration

Performing work based on the type/classification of workflow, including assignment to employees

Services

Robot agents, analytical libraries, user interfaces, etc.

Integration

Connecting new automation with legacy platforms

AI and Analytics

…

Types of problems

Classification

Regression

Dimensionality reduction

Clustering

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Recommendations

Anomaly detection

Density estimation

Advancements

Neural network models

Open-source culture

Computing power

Mass storage cost

“During our cooperation, 3Soft demonstrated a comprehensive approach to the challenges it faced. It performed the commissioned work in a professional manner, carrying it out in accordance with the arrangements.”

Norbert Górski

Director of the Systems Operation Department at ING Bank Śląski

Cash Processing

Automated cash allocation as an example of leveraging AI potential in banking

Most banks aim to integrate cash management services into a single ecosystem.

By leveraging historical data and appropriate forecasting techniques, it is possible to minimize the number of cash transfers while ensuring liquidity at every branch and ATM.

Benefits

Better management of cash reserves
Reduced liquidity risk
Cost savings through optimized logistics
Increased transparency and control over cash flows
Faster response to changing cash demand

Na zdjęciu widoczny jest zbliżenie polskich banknotów, z których najbardziej wyeksponowany jest banknot o nominale 200 złotych. Na jego powierzchni widać elementy zabezpieczające, mikrodruk, wypukłe kropki dla osób niewidomych i słabowidzących, numer seryjny „BA9448987” oraz napis „DWIEŚCIE ZŁOTYCH” wraz z godłem Polski – orłem białym. Pod spodem znajdują się częściowo widoczne inne banknoty.

Traditional approach

Manual generation of cash replenishment and withdrawals
Forecasts based on intuition
Manual analyses
Manual modification of orders and instructions for cash replenishment
Data silos
Reactive actions

Cash processing of the future

Forecasts based on advanced algorithms
Automated analyses
Automatic generation of cash replenishment orders
Automatic modification of orders and instructions for replenishment/withdrawal
Holistic data analysis
Real-time operations

Kobieta w okularach, ubrana w biało-czarną bluzkę w paski, siedzi przy biurku z laptopem, trzyma w jednej ręce smartfon, a w drugiej kartę płatniczą; na biurku leży otwarty notes, a w tle widać półki z segregatorami i dekoracjami.

The future driven by Artificial Intelligence

Genereative AI as the pillar of financial sector transformation

55%

of companies in the banking sector plan significant investments in GenAI over the next two years – an increase of 15% compared to today*.

The key, however, is ensuring that these investments are directed toward areas that deliver real business value to banking institutions. At 3Soft, this is exactly what we focus on.

*Source: NTT Data Global GenAI Report, 2025

Kobieta w jasnoniebieskiej koszuli siedzi przy biurku z laptopem, uśmiecha się i rozmawia z drugą osobą, której sylwetka widoczna jest od tyłu; w tle widać mężczyznę w garniturze pracującego przy innym biurku.

How does AI support companies in the banking sector?

01: Intelligent automation
Automatic processing of routine procedures such as identity verification, document analysis, and compliance processes.
02: Better situation assessment
AI supports credit risk analysis, fraud detection, customer segmentation, and behavior prediction.
03: Intelligent products
Creation of financial products tailored to individual customer needs – e.g., loans, insurance, or investment products.
04: Enhanced interaction
Implementation of chatbots, virtual assistants, and natural language processing (NLP) tools to facilitate 24/7 customer contact.
05: Faster case resolution
Shortening customer service and internal process times – e.g., automatic application approvals, faster complaint handling, and instant transaction analysis.
06: Cash processing
Automation of processes related to cash management: flow analysis, demand planning at branches and ATMs, and logistics management of cash transportation.

Compare possibilities and choose the right direction

Three AI implementation models for modern banking

	Hybrid	Cloud	On-premises
Characteristics	Combination of local and cloud resources	Deployment fully based on external cloud infrastructure	All infrastructure and data processing carried out locally
Advantages	Flexibility and scalability Control over critical data Cost optimization	Fast deployment Scalability Low initial costs	Full control over data High level of privacy and security
Disadvantages	Greater management complexity Requires system integration	Limited control over data location Regulatory requirements	High infrastructure and maintenance costs Longer implementation time
Operating costs	Balanced – fast MVP in the cloud, local scaling	Lower (service model, pay-as-you-go)	Higher (equipment maintenance, human resources)
Privacy maintenance	Sensitive data stored locally, the rest processed externally	Requires careful configuration	Full control and compliance with financial institution regulations