Having topped $266 billion in 2022, the global financial technology (Fintech) market could be growing at a CAGR of 17.5% for the next seven years.
Commercial and investment banks, insurance and asset management companies, credit unions, venture capital firms, and stock exchanges undergo technology transformation to accommodate the changes of the digital age.
One of the most potent technologies in the Fintech realm is undoubtedly machine learning (ML) — a subset of artificial intelligence that involves feeding various data to algorithms.
These data can include records of past transactions, historical as well as real-time stock prices, and information on economic variables such as inflation rates and GDP.
Depending on the type of machine learning technique used (supervised, semi-supervised, or unsupervised), ML-powered Fintech software can perform various tasks such as assessing the creditworthiness of an applicant, making short and long-term predictions about stock prices, recommending investment portfolio diversification strategies, and more.
In this article, a team of analysts from Symfa, a company specializing in developing financial software solutions, will identify the main areas of financial software development that benefit the most from ML.
Let’s dive right in!
5 Ways ML is Changing Fintech Software Development
Machine learning has found numerous potent applications in financial software development:
- Enhanced data and system integration. Machine learning algorithms can significantly improve enterprise application integration (EAI) and data integration processes, acting as a welcome addition to traditional extract, transform, load (ETL) instruments, APIs, and middleware. For example, ML solutions are capable of recognizing data structures and patterns, which simplifies data migration from legacy databases to modern cloud-based data storage solutions. In particular, ML can be useful in analyzing unstructured data sources, such as images, emails, and documents, which comprise up to 80% of all operational data in the banking sector. Additionally, machine learning helps eliminate duplicate records in financial software systems, monitor and maintain data quality, and ensure compliance with industry-specific data privacy regulations.
- Advanced data analytics. Building on improved data integration workflows, machine learning enables financial services organizations to access a wider range of data sources, ensuring their analytics operations are more efficient and inclusive. JPMorgan Chase, one of the largest financial holdings in the world, uses Big Data and machine learning to predict market trends and monitor factors that may influence them. Another example comes from Morgan Stanley, whose Next Best Action system helps brokers come up with investment and wealth management ideas for their clients using customer and market trends data. And 31% of financial companies already utilize ML data analytics for payment fraud detection and prevention!
- Task and workflow automation. Machine learning algorithms enhance robotic process automation (RPA) tools, transforming rule-based bots into intelligent agents, that make decisions independently instead of merely automating copy-paste operations. Such bots can be programmed to extract data from various types of documents, including invoices and financial statements. They are capable of identifying anomalies in transactions and automating entire processes, such as credit application and approval. Combined with machine learning, RPA bots can also streamline customer onboarding and Know Your Customer (KYC) tasks, validating client identities and performing background checks. According to industry insiders, IPA solutions can reduce the time required for customer identity verification from several weeks to just 15 minutes. This improvement can have a positive impact on the financial organizations’ profitability.
- Customer experience personalization. Chatbots powered by natural language processing (NLP) and generative AI technologies can provide customer support, answer queries, and assist with account management, enhancing the overall customer experience. The tasks that could be taken over by intelligent chatbots range from providing personalized financial advice to scheduling appointments and populating a financial organization’s website and mobile app with insightful content. Greater adoption of generative AI solutions driven by existing large language models (LLMs) like GPT-3 and BERT can also lower the barrier to entry for early machine learning adopters. In particular, financial organizations no longer have to train ML algorithms from the ground up. Instead, they can iteratively tweak a foundation model using their own data, test the ML proof of concept (PoC) in a limited number of tasks, and scale their efforts company-wide should the PoC prove successful. According to the Boston Consulting Group (BCG), conversational technologies can initially enhance the efficiency of the core finance processes by 10-20%. As the technology matures, it is likely to augment the majority of tasks and workflows — from customer support to contract drafting.
- Improved forecasting. ML models can assess and manage risk by predicting market movements, identifying potential anomalies, and optimizing portfolio allocation. For example, intelligent algorithms assist financial organizations in analyzing stock price data and trading volumes. The accuracy of such predictions may depend on a particular algorithm, with more advanced technologies like recurrent neural networks (RNNs) and long short-term memory (LSTM) networks displaying the best results. Similarly, NLP solutions may study industry reports, articles, and social media posts to gauge market sentiment and help financial organizations make better-informed decisions.
As you can see from the examples above, machine learning holds immense promise for financial organizations.
However, its usage is associated with several risks and challenges, including:
- Modernizing a company’s IT infrastructure for ML implementation
- Collecting sufficient amounts of data for machine learning model training and ensuring its quality
- Achieving ML model explainability by opting for simpler white-box ML solutions or introducing an explanatory layer on top of more advanced black-box systems
- Preventing ML model overfitting — i.e., situations when algorithms perform well when applied to historical data but fall short when faced with unfamiliar information
- Planning for “black swan” scenarios, such as the COVID-19 pandemic and other events that have no historical precedent
- Eliminating algorithmic bias, which might lead to ML models’ making wrong assumptions about a particular customer group
- Ensuring ML solution scalability and portability across tasks and use cases
The good news is, that most of these pitfalls can be avoided. For a financial organization considering machine learning, it is important to take an iterative and well-balanced approach to its implementation.
This involves assessing IT infrastructure, conducting data audits, identifying and prioritizing use cases, and planning an ML solution architecture for future growth while starting with small, tangible proof of concepts that can bring value faster.
And don’t forget about data preparation: after all, machine learning solutions are only as good as the data they’ve been trained on!
