AI in Fintech: How is AI Used in the Fintech Industry?

Artificial Intelligence is a non-negotiable tool that affects any operational sector across the globe by optimizing security, improving decision-making, and accelerating workflows. The industry of financial technologies is not an exception. 

According to Grand View Research, the global AI in fintech market size is anticipated to reach the threshold of $41.6 bln. by 2030. Hence, understanding the core technologies, benefits, and challenges of AI in fintech is critical for companies to remain competitive within the evolving digital economy. 

What Is AI in Fintech? (With Simple Examples)

AI in fintech represents the use of intelligent, data-driven technologies to automate core financial processes, boost security, sharpen strategic decision-making, and deliver personalized customer experiences for functions like fraud detection/prevention and algorithmic trading. The three key examples worthy of consideration are as follows:

Machine Learning

Machine Learning is a category of Artificial Intelligence that provides systems with the ability to automatically learn and improve from experience, database knowledge (if applied to CRMs), without being explicitly programmed.

The key knowledge ML models can train on includes historical data, involving transactions, market movements, and user behavior, to identify patterns. Key applications involve predicting credit risks, calculating credit scores, and identifying unusual transactional behavior.

Neural Networks

A neural network is a computational architecture, often applied within Machine Learning, that simulates the structure and function of the human brain’s interconnected neurons (nodes). Data is processed and passed through these nodes to optimize decision nodes.

Complex neural networks like Deep Learning are critical for highly accurate tasks that involve pattern recognition, like real-time facial recognition for KYC and crafting real-time algorithmic trading strategies. 

Natural Language Processing

NLP is the AI discipline dedicated to enabling computers to read, understand, decode human language, and generate subsequent responses. 

In financial technology, NLP assists in the quick processing of loads of text-based financial data, supporting key applications, like powering virtual financial assistants and customer chatbots, analyzing settlements in market news, and automatically summarizing compliance documents. 

Key Benefits of AI in Fintech 

AI drives substantial improvements within the financial sector by streamlining accuracy, security, speed, and cost-efficiency. Artificial Intelligence minimizes human error, prevents financial losses, speeds up service delivery, and promotes optimized resource allocation.

Optimized Accuracy & Error Reduction

Machine learning and deep learning models outperform human capabilities in processing Big Data within a shorter period of time. They can detect non-obvious patterns that employees or traditional rule-based systems might skip, which leads to improved accuracy in decision-making.

For instance, in credit scoring, AI can deliver a more accurate risk evaluation as compared to the standard credit score models by evaluating loads of data points, involving additional sources of information, like utility bills or phone payment history. 

Improved Security

AI models are developed to continuously learn and adapt through machine learning, as compared to rule-based security, which enables them to automatically update their threat detection patterns. As a result, this enables them to address the newly emerging malicious tactics by demonstrating proactive defense against sophisticated financial crimes and ensuring regulatory compliance. 

For instance, the AI system can detect a sudden cluster of new loan applications. It can look legitimate, but shares subtle, unusual patterns like similar IP ranges combined with modified employment histories. The machine learning model automatically identifies this new pattern as high-risk and updates its fraud detection algorithm without heavy reliance on the human programmer to update new rules. This enables banks to provide an instant blockage of the synthetic loan applications. 

Accelerated Service Provision

AI-driven chatbots and virtual assistants deliver instant customer support due to NLP. The NLP-powered tools manage routine inquiries, process account management tasks, and guide users through procedures, minimizing customer wait times and contributing to workload optimization among employees. 

For instance, in the case of a client losing a card, AI can provide the following initial actions, involving understanding the user’s intent, then verifying the client’s identity, and immediately performing required account actions (account freeze and initiating the order to replace the card). 

Reduced Costs

Artificial Intelligence empowers financial institutions to forecast consumer demand, market shifts, and operational resource requirements. The capability ensures resources are deployed with maximum efficiency, leading to streamlined capital and product management, management campaigns, and minimizing costly issues while working with too many or limited resources. 

For instance, AI analyzes historical data as well as current economic indicators, like housing market activities. Then, AI forecasts an increase in mortgage applications in the following quarter for a specific geographic zone. 

Core AI Use Cases in Fintech

AI powers the fintech industry by enabling core use cases that enhance efficiency, security, and customer engagement. From integrating smart chatbots and fraud detection to performing advanced risk management analytics, AI drives undeniable innovation, streamlining trends in fintech software development.

Chatbots & Customer Support

AI-powered chatbots and virtual financial assistants are deployed across mobile apps, IoT in banking, and websites to manage client interactions. Their primary functions include providing 24/7 support, which directly influences client satisfaction and balances the load of customer support representatives. Technologies involved: ML and NLP. 

Key Functions of Chatbots:

• Optimizing Routed Inquiries: chatbots ensure fast answers to common questions about account balances, transaction history, or fee structures. 

• Personalized Advice: AI-powered chatbots provide clients with personalized advice on saving based on clients’ spending behavior and expenditure categories.  

• Lead Routing: these solutions detect potential leads, identify the request complexity, and route them to the human agent. 

Fraud Detection & Security

AI is applied to continuously analyze loads of behavioral and transactional data in real-time to enable models to learn normal patterns, spot and flag deviations or anomalies, as compared to traditional security systems with their static rules. Technologies involved: ML and DL.

Key Functions of AI Fraud Detection & Security: 

• Real-Time Fraud Prevention: instant analysis of the risk score of every transaction, focusing on factors like location, frequency, amount, and recipient, which enables AI to block fraudulent actions before they emerge. 

• Cybersecurity Defense: AI analyzes network traffic and system logs to identify adaptive cyber threats and previously unknown attacks, to enable immediate and automated defensive responses.

• Identity Verification (KYC): this involves leveraging advanced pattern recognition to both detect fraudulent identity documents and verify identity through real-time facial recognition during the onboarding process. 

• Liveness & Deepfake Detection: during real-time facial recognition checks, AI can apply liveness detection to ensure the user is a real, live person, not a picture or deepfake, which can prevent identity theft and fraudulent accounts. 

Big Data Analytics & Risk Management

AI is deployed to process massive and diverse datasets, including public market feeds, news sentiment, social media, and interaction logs. This goes beyond traditional analysis, providing strategic insights into the hidden correlations and predictive patterns missed by human agents. Technologies involved: ML and predictive analytics. 

Key Functions of AI in Big Data Analytics & Risk Management

• Credit Risk Scoring: AI constructs precise, dynamic credit profiles by incorporating non-traditional data, including utility payments, which leads to accelerated lending decisions and optimized default prediction. 

• Compliance Risk Modeling: Artificial Intelligence analyzes regulatory documents and transaction data to model potential exposure, enabling institutions to identify areas of non-compliance with AML or other financial regulations. 

• Algorithmic Pricing: AI considers fluctuating market data and individual risk factors and adjusts the cost of financial products like loans or insurance premiums in real time. 

Automation & Paperwork Reduction

AI is applied to digitize, verify, and process both physical and digital documents within the complete financial lifecycle, from client onboarding to loan origination and compliance reporting. This promotes accelerating previously bottlenecked workflows by replacing manual data entry and visual inspection. Technologies involved: OCR, DL, and ML.

Key Functions of AI in Automation & Paperwork Reduction:

• Robotic Process Automation (RPA) Orchestration: Artificial Intelligence optimizes RPA workflows with cognitive abilities, enabling bots to make better decisions in ambiguous situations, such as interpreting handwritten information, which previously depended on human input. 

• Document Analysis & Extraction: AI automates the reading, classification, and extraction of specific data fields, including account numbers, dates, or signatures from complex unstructured documents like legal contracts, invoices, and loan applications. 

• Automated Client Onboarding: Artificial Intelligence instantly processes and validates data from digital and scanned forms, IDs, and proofs of address, simplifying new clients’ sign-ins, making them fast and error-free.

Explore fintech development trends to stay ahead of the curve.

AI Tech Stack for Fintech Products

Building an AI for fintech applications requires careful attention to the technology stack involved, as it will directly impact security, scalability, and correctness of Artificial Intelligence functionalities. The table below showcases key technologies to consider when thinking about developing AI for financial technology solutions. 

Challenges of Applying AI in Fintech (and How to Solve Them)

Despite the benefits Artificial Intelligence brings to the fintech industry, there are four top challenges we would like to discuss to prevent any misconceptions about the tool’s efficacy, to outline strategies to improve its performance, regulatory compliance, and reliability. 

Fallacies in AI Decision-Making

A major challenge in AI is that complex algorithms sometimes make biased, inaccurate, and unpredictable decisions. These mistakes can lead to compliance issues, financial losses, and damage to the company’s reputation, ultimately eroding trust. 

Solution

Addressing AI errors requires a multi-faceted approach centered on ensuring the reliability, transparency, and accountability of AI systems:

Validation: presents rigorous testing of AI models to confirm their correct performance across different conditions and make sure their outputs are accurate and consistent, which promotes oversight beyond the initial development. 

• Performance Metrics: AI models are evaluated with performance metrics like accuracy and precision to confirm they meet predefined thresholds. 

• Bias Detection: validation involves testing outcomes across different demographic groups to identify unintended biases to prevent discriminatory decisions in areas like lending or insurance. 

• Edge Case Testing: it involves challenging models with unusual or extreme data scenarios to uncover vulnerabilities and ensure they maintain robust behavior even in rare circumstances. 

Explainability (or XAI): aims to make an AI decision-making process transparent and understandable to humans by enabling AI to justify outputs. 

• Root Cause Analysis: explainability tools like LIME or SHAP pinpoint the input features that most impacted an AI’s mistaken decisions, enabling developers and experts to detect and fix data or logic problems. 

• Trust & Transparency: understanding the rationale behind AI’s lending decisions or flagged transactions promotes trust and transparency, enabling stakeholders to verify the system’s fairness and logic and build confidence in operations. 

Governance: creates the comprehensive policies, procedures, and oversight needed for responsible management of AI systems throughout their entire lifecycle, promoting accountability and ethical deployment.

• Policy Frameworks: involve creating clear guidelines for every stage (data collection, model development, deployment, and monitoring) to promote full compliance with ethical standards and legal requirements like GDPR. 

• Oversight Bodies: include dedicated committees or roles like AI ethics boards or responsible AI leads to review and approve Artificial Intelligence systems that ensure human accountability for AI decisions. 

Data Bias

Data bias is a major challenge for AI in fintech. When models learn from data that reflects social inequality or lacks representation, they can produce unfair or discriminatory results. This can also create regulatory and ethical problems. 

Solution

Addressing data bias is a central requirement for creating fair, equitable, and reliable Artificial Intelligence systems. 

High-Quality Datasets: the primary solution involves proactively guaranteeing that the data used to train AI models demonstrates high quality, representation, and fairness. 

• Diverse Data Sourcing: includes active collection and combination of data from a broad spectrum of sources, demographic groups, and historical periods to ensure the training data truly represents the intended population and various scenarios. 

• Data Cleansing & Preprocessing: involves rigorous processes to identify and correct errors, inconsistencies, and missing values. This also includes detecting and removing features that might introduce subtle bias, like zip codes associated with specific demographic groups. 

• Synthetic Data Generation: the process involves the creation of balanced and representative artificial data. This is performed to supplement or completely replace real-world datasets that are either limited or inherently biased.

Monitoring: ensures continuous oversight and evaluation of the deployed AI model aimed at detecting and addressing any emerging biases or performance declines during real-world operations. 

• Bias Drift Detection: is about implementing systems that constantly monitor AI outcome in production, alerting operators if the model starts displaying biased behavior or if its performance varies significantly across various demographic groups. 

• Performance Tracking: the practice ensures regular comparison of AI predictions against actual outcomes across different user segments to promote consistent accuracy across all involved groups. 

• Feedback Loops: human experts review AI decisions, especially those flagged as biased or ambiguous. These experts can correct the AI’s choices with their input, which is used to retain and refine models. 

Data Transparency & Regulation

Since it’s mandatory for fintech organizations to ensure compliance with regulations on data usage and fairness, it introduces a challenge for AI adoption, as the lack of data transparency can lead to legal penalties, fines, and reputational damage. 

Solution

Institutions within the financial technology sector have to run their operations within the compliance systems that should promote continuous synchronization of AI governance with existing regulations. 

GDPR, or the General Data Protection Regulation, is a legal framework that obliges organizations that deal with transferring, processing, and storing EU citizens’ sensitive data to ensure data protection and enforce security, penalizing any non-compliance. 

Fintech companies have to provide meaningful information about the logic involved, which is technologically complex for AI systems, and increases the need for Explainable AI approaches.

• Data Minimization: the principle that dictates organizations to use AI systems to collect and operate only essential data. 

• The Right to Be Forgotten: although this principle obliges companies to delete clients’ information by request if it’s no longer necessary, it imposes a challenge for AI. Initially, an Artificial Intelligence model trains on large datasets, which may involve personal information. Removing individual information from the dataset may require retraining or fine-tuning the model to ensure the client’s data is no longer used to train AI. 

PSD2 (Revised Payment Services Directive 2) Compliance: is a regulation that requires Strong Customer Authentication during online payments. When in progress, the online payment must apply two out of three elements of identification, like something that the user knows (PIN or password), something that the client has (device), and something the client is (biometrics). 

• SCA Optimization: AI is applied to evaluate transaction patterns to spot anomalies and optimize transaction workflows by marking suspicious behavior patterns before the payment is finalized, all within SCA requirements. 

• Third-Party Provider (TTP) Data Access: AI tools are built to operate using secure, PSD2-connected APIs that only enable third-party providers (TPPs) access to customer account data if the user provides consent. 

AML (Anti-Money Laundering): the central difficulty lies in the challenge of implementing data-intensive Artificial Intelligence for financial crime detection and obligatory adherence to laws regarding data handling and privacy. 

• Transaction Monitoring: AI continuously analyzes vast amounts of transactional data, identifying suspicious patterns and anomalies that might indicate money laundering, far exceeding human capacity.

• Sanctions Screening: AI systems efficiently screen customers and transactions against global sanctions lists, reducing false positives and improving the accuracy of detection.

Integration & Implementation Complexity

One of the main difficulties with fintech is AI complexity. Whether it is the model itself, the processes of data management, scaling, and integrating with legacy systems, or the existence of siloed data. 

Solution

When it comes to integrating complex AI systems into legacy systems, strategic architecture and deployment decisions need to focus on flexibility, interoperability, and scalability.

Modular Architecture designs AI systems as independent, interchangeable components instead of a single extensive application. Each model performs a specific function and can be developed, deployed, and tested autonomously. 

• Reduced Interdependency: this implies that if updating or troubleshooting a single component will have less impact on the overall system, it simplifies maintenance and reduces the risk of widespread failures. 

• Simplified Integration: new AI capabilities can be introduced or exchanged as separate modules, which alleviates their addition to existing legacy systems without requiring a full system overhaul. 

• Scalability: individual high-demand modules can be independently scaled up or down, which streamlines resource utilization. 

APIs: Application Programming Interfaces serve as standardized communication bridges, enabling different software systems or components to interact. In the context of AI, this implies that APIs provide defined methods for other applications to request and receive services from an AI model. 

• Seamless Interoperability: ensures AI models can be effortlessly plugged into existing financial applications (for instance, CRM systems) without requiring those applications to be rewritten. 

• Abstraction: implies that developers can utilize an AI service without the need to understand how to use its API, not the intricate underlying complexity of the Artificial model itself. 

• Standardization: makes sure APIs provide a consistent method for different systems to communicate, thereby minimizing integration effort and reducing errors.

Cloud-Based Artificial Intelligence: means utilizing AI services and infrastructure managed by cloud providers (like AWS, Azure, or Google Cloud), frequently encompassing pre-built AI models, development tools, and scalable computing resources. 

• Reduced Infrastructure Burden: implies that financial institutions bypass significant investments in costly hardware, data centers, and specialized IT personnel for hosting AI models, as the cloud provider handles these resources. 

• Scalability on Demand: enables AI resources to be dynamically increased or decreased according to fluctuations in demand, a vital feature for managing variable fintech workloads. 

• Faster Deployment: is enabled through access to pre-trained AI models and managed services, which considerably accelerate the development and deployment of AI applications. 

Why Choose Agilie as Your AI Fintech Partner

Agilie is a mobile and web software development company, specializing in custom product development and project reengineering. With cross-industrial expertise in fintech, healthcare, marketing, and real estate sectors, our IT experts are open to time-sensitive changes even at the late development stages and deliver high-quality results in the end. 

Agilie can deliver end-to-end fintech development services and AI software development, including a RAG-powered chatbot, workflow automation, and custom AI solutions for clients’ bespoke systems. Agilie has hands-on experience with LLMs, automation, and enterprise integration to help businesses improve operations, enhance the customer experience, and catalyze data-driven performance. 

Summary

AI in the fintech industry shifts from being only a supporting tool to an indispensable foundation of modern fintech, enhancing security, efficiency, and customer experience. Organizations have to integrate Artificial Intelligence strategically, focusing on robust governance, data quality, and explainable models to mitigate core risks such as data bias or non-compliance. Hence, when considering integrating AI into your business, you have to select a truly reliable partner to align AI with your business needs. 

Interested in integrating AI into your project? Agilie is ready to help!