Artificial intelligence and machine learning are paving the way for new developmental possibilities in the financial & technology industry. When it comes to the financial services industry, Artificial Intelligence (AI) and Machine Learning (ML) are changing the landscape. The benefits to both consumers and FinTech businesses are numerous and include more efficient processes, better financial analysis, customer engagement, and better customer service.
The distinctions between artificial intelligence and machine learning
Artificial intelligence (AI) is a subset of the more significant subject of Computer Science that allows computers to solve issues that were previously handled by human labour. In today’s culture, it is used as an umbrella word for computers that can mimic human intellect and has many uses, one of which is machine learning.
Machine learning (ML) is an application of artificial intelligence that allows computers to automatically learn from data and improve from experience without being explicitly programmed. Machine learning may assist in generating, managing, and interpreting data, resulting in actionable insights.
Top Benefits of AI and Machine Learning in FinTech
There are many advantages of using Artificial Intelligence and Machine Learning in the financial services sector, which are listed below. Companies that utilize artificial intelligence and machine learning to develop prediction models, rather than depending only on human employees, can process massive quantities of data, improve working procedures, and minimize fraud.
It is less skewed
Bias is something that humans are inherently prone to. They may unconsciously make selective use of facts or make intuitive judgments about other individuals based on factors such as age, gender, or race, among other things. In the vast majority of situations, artificial intelligence will be less prejudiced than humans. That does not imply that artificial intelligence is entirely objective. In the case of data that has been consistently skewed during training, the algorithm will produce biased judgments.
Less time-consuming
AI/ML is less time-consuming than manual procedures because models are updated in near-real-time, if not real-time, as they are learned. Integration of a model into an automated decision-making system allows a model to accurately anticipate the behaviour of millions of users in a matter of seconds. If the predictive models were maintained manually by humans who made the same decisions, it would be prohibitively costly to produce the same processing power. This advantage is helpful when making complicated choices about financial planning, such as investment decisions.
Much more cost-effective
Predictive models increasingly replace or augment human skills because they can make judgments more quickly and cost-effectively than human specialists. Because the updates are performed by ML algorithms rather than by people, artificial intelligence and machine learning are typically less expensive to implement than their human equivalents. The initial investment and ongoing maintenance expenses are minimal compared to the price of employing highly educated human specialists.
Increased scalability
Artificial intelligence and machine learning are capable of managing vast collections of micro-segments. In the context of artificial intelligence-driven micro-segmentation, breaking up substantial consumer clusters produced by conventional macro-segmentation methods is defined as allowing businesses to engage with customers in a more individualized and tailored manner.
However, even though digital banking was already well-established before the start of the coronavirus epidemic, recent events have accelerated a fast change in the industry. There has been an increase in demand for online banking services, with artificial intelligence (AI) and machine learning (ML) being among the most critical factors driving development and sustainability in the sector.
5 Impacts of AI and Machine Learning in Fintech
Automated approvals of loans
The need for financial help will continue to rise as individuals fight to regain their companies and lives as part of the epidemic. Consumers want to start their recovery to everyday living and financial health while companies do their utmost to keep flooding and prosper today and, in the years, ahead. These changes in the market led to an increased interest in the “just-in-time” lending paradigm, which cannot be achieved just via manual processes.
The effort and time required to assess and approve borrowing applications have been one of the most significant difficulties for lenders. Manual underwriting is undoubtedly a tedious operation but maybe primarily automated by using specialized AI software.
Detection of fraud
Hundreds of millions of dollars a year cost the US economy illegal transactions, with fraudulent wire transfers alone representing an annual loss of $439 million. More than 270,000 cases of credit card theft occurred in 2019, more than tripling in the last two years. This is the right sector for Financial Services firms with around 26 percent of AI start-up investment for fraud and cybersecurity in the banking industry—more than any other instance.
In light of the vast size of the financial activities of today, it is not feasible to manually examine all transactions for suspicious or possibly wrong behaviour. But financial firms can adopt a more in-depth and sophisticated approach to detecting credit, payment, and account opening fraud using AI to monitor transactions in real-time.
Customer Support AI-assisted
Customer expectations are shifting and growing with the advent of digital banking. People anticipate rapid reactions from lightning and are used to banking every day, even nights and weekends. Financial institutions thus need to be made accessible 24/7 to give answers and make everything from transactions to credit applications easier.
Call centres and Customer Service Teams often have to deal with huge backlogs and provide the experience customers anticipate. Cost constraints frequently prohibit more personnel from being recruited. Although the hot nature of many financial services still needs in-house assistance, AI-powered chatbots can perform many things very well.
Conversational banking voice-powered
Voice-enabled gadgets such as Amazon Alexa or Apple Siri may save time and increase the efficiency of daily activities. In reality, NLP technology has developed to make it trustworthy for conducting fundamental financial moves, like chatbots but utilizing a speech interface. While some individuals may be reluctant to use speech recognition and banking instructions as voice-controlled interfaces grow more ubiquitous in daily life.
Despite the consumer confidence issues, Fintech has made significant advances in providing voice-powered banking solutions, particularly given the growing demand for contactless payments due to the epidemic.
Next-Gen Trading Algorithms
Introduced in the 1970s, algorithmic trading uses predetermined rules-based instructions in stock market trade, which big trading companies and institutional investors have widely used. In recent years, AI has been changing the trading desk and helping crush millions of data points in real-time while learning and gaining insights that more conventional statistical models have been unable to detect.
In recent years, AI has done a great deal to bring algorithm trading to people. Consumers now have access to usage-friendly smartphone applications that allow them to trade stocks and shares using AI’s decisive decision-making.
Today 70 to 80% of the transactions are algorithmically performed. However, IA and machine learning take algorithmic trading to the next level by decreasing risks and making decisions more informed. An AI system can react faster to a changing trade environment (e.g., the pandemic) and collect and account for anomalies. This is because a machine learning model is not static—it continuously contains and learns from new data.
Final Thoughts
Sometimes interchangeable artificial intelligence and machine learning, but they have distinct meanings. AI is a machinery-based umbrella term for simulating human intelligence, whereas ML is an AI subset. AI/ML-driven predictive models may help companies improve revenues by expanding data analysis efforts to get more insight into volume, quality, and speed. If you want to use AI solutions to improve your organization’s business operations, it may be helpful for an expert with in-depth knowledge and expertise.
