Most fintech business models heavily rely on using Artificial Intelligence (AI) to automate and personalize financial services, improve risk management, and detect fraud, among many other things. It is no longer news that AI has great potential for the present and future financial industry. For example, AI is one of the major emerging technologies driving the growth of financial software development services worldwide.
The focus of this article is to highlight the significant ways in which fintech companies are harnessing AI. We hope these insights spark innovative ideas for your business.
AI in Fintech
AI has taken centre stage in building fintech products since the beginning of fintech. It initially presented the opportunity to analyze massive amounts of financial data using natural language processing (NLP). But now, AI tools are expected to be adopted in the whole journey of digital banking operations. From customer onboarding to seamless transaction processing and ongoing customer support, AI provides numerous opportunities to increase financial services’ speed, precision, and efficiency.
AI is one of the seven technologies expected to define fintech’s future. We are already seeing more investment in AI startups. In 2023, the global AI fintech market grew at a compound annual rate of nearly 27%. Other technologies to shape fintech are Blockchain, Internet of Things (IoT) and Cloud computing, No-code development platforms (NCDPs), Open source software, and Process Automation (RPA).
Use Cases
Let’s explore some examples that offer a glimpse into applications of AI in Fintech
- Security & Fraud Detection
- Asset Management
- Personalized Banking Services
- Credit Scoring and Loan Prediction
- Enhanced Algorithmic Trading
- Insurance
Security & Fraud Detection
FinTechs’ dependence on technology and data-driven processes for financial services makes it a highly risk-sensitive industry. Adopting AI-driven security and fraud detection is instrumental in addressing this particular challenge. This AI tool leverages user entity behaviour analytics (UEBA) to provide timely alerts on suspicious activities and help businesses trace and investigate the sources of security threats.
Use Case: Moonsense
Moonsense provides an AI-driven identity theft detection system for businesses, including financial service providers, to address fraud such as Synthetic ID. The system is built on UEBA and other intelligence solutions. In a recent report by FinTech Global, the startup announced that it’d secured $4.2m in seed funding to improve the AI fraud detection tool further.
Asset Management
A 2020 report by Research and Markets indicates that fintech advisor products will significantly contribute to the growth of the global use of AI in asset management. The entire market is expected to reach $8.3 billion by 2026. From the said analysis, asset managers were found to be exploring the numerous opportunities in AI for managing investment portfolios, risk and compliance, data analysis, process automation, and conversation platform.
Use Case: Akros Technologies
Akros Technologies developed portfolio management as a service (PMaaS), an AI-powered operating system for portfolio management. Trained on GPT-3 natural language processing (NLP), the system provides automated analysis for exchange-traded funds (ETFs), eliminating the reliance on human efforts.
Personalized Banking Services
Forget personal finance apps for a second; personalization is in demand in today’s core banking services more than ever. Enabled by Al algorithms, financial products are designed to consolidate each customer’s specific needs. A significant example is how conversational bots and virtual assistants are getting more intelligent and popular.
User Cases – Stripe
Like many financial Saas providers, Stripe uses customers’ transaction records and spending habits to create personalized messages and design financial products. This is made possible by utilizing predictive and behavior analytics. In addition, the B2B company also provided AI-powered chatbots to help customers make meaningful financial decisions.
Credit Scoring and Loan Prediction
Using machine learning models to determine risk appetite and tolerance is becoming mainstream in credit scoring and loan prediction. Apart from enabling accuracy and speed, this technology reduces bias common to traditional methods.
Use Cases: FICO
FICO developed an AI-based system called FICO Origination Solution to enhance credit risk scoring. With a combination of credit risk models and AI/ML technology, the system helps lenders make prompt origination choices without compromising risk. Chief analytics officer at FICO, Dr Scott Zoldi, sums up the technology’s benefits in the following statement.
“To build the models in FICO Origination Solution, our data scientists used AI and machine learning algorithms to discover a better way to segment the scorecards. This allows us to apply AI to improve risk prediction without creating “black box” models that don’t give risk managers, customers, and regulators the required insights into why individuals score the way they do.”
Enhanced Algorithmic Trading
AI-driven algorithmic trading uses machine learning to execute stock trades based on programmed instructions. Also known as Algo trading (AT) or high-frequency trading (HFT), it makes trade decisions that consider important market trends and minimizes risks of failures.
Use Case: Groww
According to information on the Google Cloud page, Groww is India’s fast-growing online investment platform that offers a simple and easy way to invest in stocks, direct mutual funds, IPOs, ETFs, and digital gold. It uses AI/ML in image processing to automate workflows and reduce trading mistakes for users making investment decisions on the platform.
Insurance
In the insurance industry, AI-powered underwriting will transform risk assessment and decision-making. Based on recent developments, AI will allow insurance services to change from reactive solutions to proactive ones by 2030. This shift is expected to be supported by seamless data generation from connected devices integrated into insured properties.
Use Case – Tractable.ai
Tractable creates AI-based insurance solutions that help businesses and individuals monitor and detect potential risks or incidents in real-time. Its technology allows users to evaluate automobiles and landed properties using computer vision. An international company, Tractable has helped over 1 million households since its establishment in 2021.
Summary
The primary reasons for integrating AI in fintech products are to enhance customer experience and stay relevant in the financial industry. But the power of AI tools to provide detection mechanisms, streamline operations, and enable data-driven insights have become a significant phenomenon in the financial sector, and experts believe it will only get stronger in the coming years.
