The demand for data science in fintech has been consistently increasing with financial technology development providing companies with key advantages in this competitive market. AI, ML, and Data Science tools can address 5 out of the 6 top risk priorities identified by the CROs of major financial institutions . We will show how data science tools enhance fintech's operational resilience by improving cybersecurity and fraud detection technology, credit risk evaluation algorithms, regulatory compliance survey systems, and innovation in financial services.
Let’s explore how data science in fintech enhances operational resilience and gives companies a competitive edge.
Enhancing operational resilience
Data science is not a passing trend and has been instrumental in the development of financial services. In 2022, across both banks and fintech companies globally, over a third reported using ML for automation, credit scoring, cybersecurity, and fraud detection. Let’s look at how data science helps them identify and mitigate these risks.
72% of CROs in the financial sector list cybercrime as the number one risk priority in 2024 and 35% report currently using data science in fintech cybersecurity. Cybersecurity is essential for maintaining trust in the fintech industry. In addition to direct financial losses, many fintech business models rely on presenting themselves as reliable partners to other institutions.
Algorithms analyze transaction records, user behavior, and network traffic to detect unusual patterns that may indicate a cyberattack. Moreover, if trained on behavioral data, they can recognize a sudden change in an employee's access pattern. These anomalies can be flagged for investigation. Behavioral analysis is used in developing advanced authentication systems that ensure that even in the case of a data leak, there are other safeguards to prevent unauthorized access and security breaches.
Fintech companies also use data science to evaluate vulnerabilities in their systems and applications. Automated scanning tools can detect weaknesses in software, configurations, and infrastructure, enabling prompt remediation before they can be exploited by cybercriminals.
The challenge in detecting modern financial fraud lies in its ability to become more sophisticated and outpace rigid detection methods. 60% of big fintech CEOs identify ML and AI as the most efficient way to combat money laundering  among other fraudulent activities.
Algorithms identify patterns and detect anomalies that differentiate normal user behavior from a potentially malicious scheme.
Another feature data science brings is network analysis. It is used to detect fraud schemes that involve complex patterns of connections between accounts and transactions. Analyzing networks helps identify coordinated fraudulent activities that are hard to detect with traditional methods.
Data science in fintech offers significant advantages over rule-based fraud detection systems:
- Keeping consistently accurate. Algorithms constantly evolve, adapting to new fraud patterns and techniques to ensure that fraudsters stay behind in the technological arms race.
- Real-time detection and prevention. Machine learning models can process transactions as they occur, providing immediate analysis and response. This is critical in the fast-paced fintech environment where delayed detection can lead to substantial financial losses.
- Scalability. As fintech companies grow and transaction volumes increase, these systems can scale accordingly, maintaining their effectiveness and alert security systems.
Read more: AI in Fintech: Use cases and best practices
Credit scoring and risk assessment
Credit risk is the second most reported risk among CROs of financial companies. The magnitude of credit risk is increasing as the economy enters a recession. Common risk-assessment strategies do not adapt to a contracting economy and might not represent the actual circumstances. This emphasizes two key financial sector needs: improving credit scoring methods for precision, and quickly adapting to changing economic conditions. Both challenges can be effectively addressed through data science.
The use of a data-driven approach for credit ratings is often cited as a key factor in the fintech gaining prominence. Going beyond reliance on credit history, fintech companies include a wider range of factors in credit assessment algorithms. This approach promotes inclusivity and accuracy, allowing companies to expand their customer base while also reducing risk.
Maximizing credit risk assessment accuracy involves accessing dynamic global economic data. Data science helps in collecting, processing, and analyzing this data to better understand macroeconomic trends. For example, during economic downturns, data science can identify industries or regions with increased credit risk, prompting lenders to adjust their lending criteria accordingly.
Meeting regulatory conditions
Data science tools assist in automating compliance monitoring and reporting. Regulatory environments are constantly evolving. Data science tools can scan and analyze regulatory updates from various sources, helping companies adapt their compliance strategies promptly. The complexities of financial regulations and the vast amounts of data that companies must handle make these tools indispensable.
Data science tools can automate the collection, processing, and reporting of data, ensuring accuracy and timeliness. This automation is essential for compliance with regulations like the General Data Protection Regulation (GDPR) or the Sarbanes-Oxley Act (SOX).
Expanding services in fintech
As more customers embrace digital banking and financial services, fintech companies are leveraging advanced technologies to create innovative solutions that provide convenience, efficiency, and personalized experiences. Both personalized financial services and algorithmic trading rely on advanced data science to provide fintech company customers with the greatest possible value. Let’s look at how it does that.
Personalized financial services
As other industries offer increasingly personalized services, customers see a lack of personalization as a serious flaw. Only 8% of consumers say that personalization in financial services is not important. The new generation of consumers expects the communication with their financial service provider to be concise and tailored to their knowledge level and interests . Which includes not overwhelming them with options and only suggesting the options that might be interesting to them.
Deep learning models excel in analyzing various customer data that is often inaccessible for other forms of analysis. They can track a customer's behavior and preferences over weeks, months, or even years, leading to more precise personalization. By tailoring financial products and services to individual needs, fintech companies can increase conversion rates, leading to more sign-ups, transactions, and revenue.
Fintech companies can use these models to suggest not only product recommendations but also specific financial steps to help customers achieve their life goals. For example, they can provide a savings plan for an upcoming holiday or advise customers on optimizing their financial decisions.
Predictive analytics for investment
Data science is the underlying methodology behind modern trading and investment management. 70% of all trading activity in the US is done by algorithms, and its volume is estimated to increase by an additional 10% by 2026 . Algorithms and models use historical data to evaluate assets. Factors such as risk tolerance, investment goals, and historical performance are weighted to suggest the most suitable combination of assets for a given investor or fund. This ensures that the portfolio aligns with the investor's objectives while maximizing profit.
Some fintech firms employ algorithmic trading, where predictive analytics is at the core of their operations. These algorithms execute trades based on predefined rules and predictive models, aiming to capitalize on short-term market movements.
Young and rapidly expanding fintech companies consistently strive for solutions that not only work but represent the best answer to every challenge they face. Data science aligns perfectly with their approach. These tools empower fintechs to use data-driven insights, improve their services, and make informed decisions that meet the changing needs of their customers. This commitment to data-driven excellence allows fintechs to stay ahead and remain competitive in the fast-changing financial landscape.
Why choose N-iX to disrupt the financial services industry?
- N-iX has been developing fintech solutions for both leading fintech companies and startups for 21 years now.
- We understand the critical importance of security and compliance in the financial industry, N-iX ensures secure development and operations, adhering to the latest security and compliance practices.
- N-iX's portfolio includes B2B payment engines, P2P lending platforms, e-commerce solutions, and FX brokerage software, among others. Our design of unique solutions based on microservices ensures fast development, easy maintenance, and smooth third-party integrations.
- N-iX adheres to efficient development methodologies like Agile (Scrum, Kanban), code review, and continuous integration to deliver high-quality products consistently.
- “Seeking stability within volatility: How interdependent risks put CROs at the heart of the banking business.” EY & the Institute of International Finance (IIF), 2022.
- “On the frontline, fintechs vs money laundering” LexisNexis & The Economist, 2019
- “Wealth Management Digitalization changes client advisory more than ever before”, Deloitte
- “Global Algorithmic Trading Market to Surpass US$ 21,685.53 Million by 2026”, BusinessWire