FinTech (short for financial technology) includes a set of technological solutions that simplify, improve and automate financial operations. For example, a custom application for loan management and a business intelligence mobile application for banks are considered fintech solutions.
FinTech Market Overview
Fintech is a promising field that has seen an increase in companies and funding in recent years. Banks are often seen as key players in the market when it comes to funding fintech projects. However, technology giants like Google, Microsoft, Amazon, and others also influence the market greatly. They are the major consumers of fintech applications that allow them to process their numerous transactions.
Enterprises are also getting increasingly interested in risk management in the finance domain. It’s a well-known fact that the economy is very prone to changes like recessions, new laws, exchange rates, interest rates, changes in supply and demand, and even environmental changes. It’s an interesting and rewarding field to make predictions. Enterprises and startups often implement a risk-management policy to protect their finances. For that, technology offers many opportunities for companies to improve their risk management approach.
So, in recent years, there has been a boom in both fintech startups and enterprise financial management optimization. The latter is caused by the increased demand for enterprises to automate their financial flow.
In 2023, the fintech market is estimated at around USD 179 billion. According to Statista, the Americas is the region that attracts the most investments.
Risk Management Components
Fintech companies should be the first to know how to manage risks across all aspects of their business. Risk management for Fintech typically mitigates the following types of risks:
- Regulatory risk – compliance with regulatory changes and expected changes in regulations applicable for fintech companies;
- Software risk – investments in software development and implementation, choosing the right software provider, maintenance of existing applications;
- Information security risk – data security, protection from data breach and unauthorized access, security protocols that offers protection of the company’s systems from hacking;
- Operational risk – hiring employees and negotiating the salary, renting and maintaining offices, as well as ensuring smooth operational workflows.
Risk Management with Technology
So, what can technology offer when it comes to financial risk management? A spoiler is that it’s mostly related to big data and AI.
Let’s start with the definitions to ensure we’re on the same page. Big data solutions are cloud-based solutions that allow parallel processing of huge amounts of data, and this data doesn’t need to be structured, which means that pictures, videos, and text can be processed. When people mention Artificial Intelligence, they mostly refer to the trained Machine Learning models. After receiving sufficient training on the required data, ML models can autonomously classify new data they haven’t previously been exposed to. AI can recognize and split data into groups, see patterns in data, and make predictions based on the data trends available to it.
AI needs a lot of data to learn and to make predictions more accurately, and this is why ML models often work within big data solutions. Compared to manual predictions, AI provides better forecasting accuracy, especially when it comes to non-linear relationships.
AI interconnected with big data can improve and streamline variable selection as well as data segmentation.
Let’s explore how big data and AI can help with risk management.
The safety of personal finances reached a new level when AI was introduced to banking systems. Machine learning algorithms monitor transactions and notice anomalies in data that can be a sign of fraudulent activity. For instance, AI can notice unusual volume or geographic location of transactions, thus ensuring the safety of clients’ money.
Credit risk modeling
AI-powered big data software has the power to analyze many variables and notice trends incredibly fast. At the same time, it’s very accurate in the predictions. Machine learning models are fed with data related to credit history, salary, and biographical characteristics, after which the estimate of risks is provided.
Before making finance-related decisions, companies often analyze market trends. It’s known to be a long and tedious process, but everything changed with the introduction of big data. Big data analytics allows stakeholders to gain insights even from such complex sources with multiple variables. Be it competitor analysis, trader behavior, or portfolio optimization, AI-powered solutions provide better ways to make decisions.
Fintech is clearly on the rise, attracting investments for a variety of purposes, one of which is risk management. Powered with new cutting-edge instruments like AI and big data analytics, financial risk management has reached a new level of accuracy. A variety of companies, from banks to manufacturers, have begun to understand the value it provides and incorporate automated risk management practices into their workflow.