By Anastasiia Molodoria, AI Team Leader at MobiDev
Growth, cybersecurity, keeping up with innovations, and regulatory compliance are major challenges that fintech startups face today. One of the great ways to overcome them is to adopt Machine Learning and Big Data. This article will show the benefits of implementing ML in your fintech startup to boost your business growth.
Enhanced Security with Fraud Prevention Mechanisms
A strong security system is a must for any fintech startup. For example, Visa’s fraudulent activity dropped to less than 0.1% of its global transactions in 2019, thanks to the implementation of an ML-supported analytics system. Machine Learning algorithms are able to detect patterns of malicious activities, detect anomalies and respond to them in a timely manner. Moreover, ML is capable of making predictions based on previous cyber-attack datasets thus identifying system vulnerabilities that could lead to attacks in the future.
Using machine learning algorithms will allow you to:
- Process transactions and other data at a much faster speed.
- Detect fraud patterns at a high accuracy rate.
- Predict new fraudulent trends.
- Fully automate various financial processes for zero human error and internal fraud.
Choosing the right ML fraud detection system for your fintech startup can give you an advantage over fraudsters and thus preserve your reputation, customer data, and financial assets. For example, classification algorithms can be applied to identify different classes of network attacks such as scanning and spoofing, while regression can help with fraud detection by determining the likelihood of fraud based on various characteristics such as location, number of transactions, etc.
Improved Customer Support with Automated Workflow
It’s obvious that customers will abandon an app if it’s not fast, convenient, and straightforward enough or if they cannot get timely support from a company when such difficulties arise. Adopting machine learning algorithms for customer service systems can improve user experience and service through automation, personalization, and data analytics.
Here’s how this can boost your startup’s customer service:
- Automated support provides 24/7 effective and fast assistance for customers. It can range from a chatbot that answers queries to a virtual assistant that suggests personalized plans.
- Predicted behavioral patterns. ML-based data analytics system helps you predict your customers’ behavioral patterns based on historical data, allowing you to offer help or additional services in a timely manner.
- Personalized experiences. Machine learning empowers a customer-focused business through highly personalized interactions and relevant solutions. For instance, ML-based recommendation engines can suggest additional solutions for your customers based on their needs and interests.
- Improved brand experience. By efficiently processing your customer preferences and needs, you can improve your business positioning, messaging, pricing strategy, and more to enable an enhanced brand experience.
Attracting and retaining customers is not an easy task, as the average annual retention in fintech is only 16%, according to the 2021 Mobile Customer Engagement Benchmark Report. Automated and personalized customer support is a way to improve user experience with your fintech platform and keep in touch with your clients.
Data-Driven Financial Decisions with Advanced Analytics
Data-driven decisions help businesses upscale their customer base, compete with similar service providers, and retain clients. However, data processing can be quite challenging for fintech businesses. With unparalleled automation and forecasting abilities, ML-based analytics can offer fast, scalable, and accurate data processing both for your fintech company and for end users.
For example, if you are creating a personal finance app, the machine learning module will allow you to enable a more detailed analysis of expenses and income, and provide meaningful insights for setting financial goals and tracking achievements. With statistics and modeling techniques, your application will be able to predict future expenses and revenues based on the historical transaction data of your user accounts.
Statistical models that accurately forecast future costs and revenues for businesses can be created by transaction enrichment with additional information such as customer demographics, purchase history, and behavior patterns, providing useful insights and assisting them in making decisions.
With ML-powered analytics, you can do the following:
- Collect large volumes of data in less time, and interpret and implement the collected data accurately.
- Translate the collected insights into actionable plans through high-accuracy predictions.
- Empower your financial app with a powerful analytics module to provide valuable financial insights to your users.
Data-driven decision-making is essential to your business growth, as well as for the success of your customers. By meeting the needs of your clients, you gain significant competitive advantages and become in demand in the market, which is an integral element of scaling and growth.
Effective Risk Management with Predictive Models
AML risks, credit risks, market risks, and regulatory risks are all threats to fintech business development. Qualitative risk assessment and effective risk mitigation strategies are what every business owner is looking for. By implementing machine learning, you can add value to financial risk management applications.
Here’s how implementing ML predictive models can enhance risk management in your fintech startup:
- Reduction in credit and fraud losses thanks to ML fraud detection.
- Effective mitigation of customer and merchant risk through enhanced identification and analysis of customer/merchant attributes, such as occupation, industry, size of transactions, source of income, etc.
- Autonomous and continuous adaptation of risk management programs.
- Assessment of market compliance and prediction of possible changes.
- Automated credit scoring based on historical data as an alternative to traditional credit scoring models.
Machine learning models learn from data, but the creator of the model can still make some choices based on the specific problems they want to solve. There is no universal machine learning solution, and each method has its pitfalls. However, partnering with an experienced team of AI engineers will allow you to choose the best option for your case and get the most out of machine learning for financial risk management.
Driving Revenue with ML Forecasting
Forecasting capabilities of machine learning allow you to build more effective sales strategies and increase revenue. There are several machine learning approaches to help with forecasting and future sales predictions. Their implementation allows for improving a number of processes such as supplier relationship management, customer relationship management, marketing campaigns, etc.
Integrating ML forecasting in fintech can drive revenue through:
- Accurate financial insights and planning
- Investment and financial predictions
- Accurately predicting future growth to make informed budget decisions, allocate resources more efficiently, and build plans to achieve your goals
- Creating demand forecasts to respond to changing market conditions and consumer behavior in real-time to make better business decisions
Improved forecasting allows you to react in a timely manner to any market fluctuations and change strategies to achieve your business goals. Having accurate forecasts of demand for your services and sales in specific periods of time, will allow you to adjust your business to the situation in order to achieve maximum revenue. For example, Bank of America announced the launch of CashPro Forecasting, an ML-powered forecasting tool that uses ML models based on a business’s cash flow history to create accurate financial forecasts and help companies predict future cash needs.
The boons of adopting machine learning range from enhanced security to increased profits. But before you can reap these benefits, you should know that no two businesses are the same. Choosing an algorithm and building a model that will allow you to achieve your goals requires studying your specific business environment. Machine learning is a flexible technology that can be considered an art, which is why ready-made solutions are unlikely to fully meet your business needs. Remember that an experienced development team will help you find a creative and innovative solution that meets the specifics of your business, enhancing your growth points and minimizing risks.