Artificial intelligence and Machine learning are pioneering new opportunities for growth in FinTech.
The use of Artificial Intelligence (AI) and Machine Learning (ML) is changing the landscape of the financial services industry, with exceptional benefits to both consumers and FinTech businesses including more efficient processes, better financial analysis and customer engagement.
According to an Economist Intelligence Unit adoption study, 54% of Financial Services organizations with 5,000+ employees have adopted AI. The report also found that 86% of financial services executives plan on increasing their AI-related investments through 2025.
Know more in our latest eBook: Encora's Key Factors to Fintech Success.
Differences between AI and ML
Although Artificial Intelligence and Machine Learning are sometimes used interchangeably, they are different.
AI is a part of the greater field of Computer Science that enables computers to solve problems previously handled by human labor. It is an umbrella term for machines that can simulate human intelligence and has many applications in today's society, which includes ML.
ML is an application of AI that provides systems the ability to automatically learn from data and improve from experience without being explicitly programmed. ML can help to generate, manage and make sense of data, providing meaningful insights.
Benefits of AI and ML in Finance Services
There are several benefits of using Artificial Intelligence and Machine Learning in the financial services industry. Companies that use AI/ML to improve predictive models, rather than relying solely on human workers, are capable of processing enormous amounts of data, optimizing the working processes, and reducing fraud. The following are examples in which Machine Learning technologies are being used to innovate the customer service experience within financial sectors to propel the industry forward.
Benefit #1: Less biased
Humans are naturally prone to bias. They might subconsciously make selective use of data, or make intuitive decisions about other people based on age, gender, or race. In most cases, AI will be less biased than humans. That doesn’t mean that AI is completely objective. When an algorithm is trained on data that is systematically biased, it will make biased decisions  . Organizations will need to stay up-to-date to see how AI can improve fairness, and where a combination of ML and human intelligence can reduce bias. This may be particularly useful in loan servicing areas and determining an appropriate credit level without human bias.
Benefit #2: Less time consuming
AI/ML is faster than manual processes, because models get updated in near-real-time, or possibly real-time. When integrated into an automated decision-making system, a model can predict the behavior of millions of users in seconds. It would be too expensive to generate the same processing power if the predictive models were manually managed by people making the same judgments. This benefit is useful in making complex decisions regarding financial services.
Benefit #3: More cost-effective
Predictive models are replacing or complementing human capabilities because they can make faster, and therefore cheaper, decisions than those made by human experts. AI/ML is often cheaper to deploy than their human counterparts because the updates are done by ML algorithms rather than by humans. The initial investment and maintenance costs do not come close to hiring highly trained human experts.
Benefit #4: More scalable
AI/ML is capable of handling large sets of micro-segments. Artificial intelligence-driven micro-segmentation is the process of breaking up large customer clusters created from traditional macro-segmentation techniques, enabling companies to interact with customers in more personalized and customized ways. Micro-segmentation means better probability of conversion rates and better targeting.
Benefit #5: Improves customer engagement
By leveraging AI to understand the customer better, taking advantage of real-time decision-making and predictive analysis, customer engagement can be improved. Using product recommendation engines, for example, has proven effective at delivering a personalized experience and driving up revenue. Product recommendation engines are a special application of AI designed to provide suggestions for each user based on a number of factors, including past behavior, in-session behavior, product economics, and the behaviors and preferences of similar users.
Benefit #6: Improves fraud prevention
Global fraud is evolving quicker than banks can respond. Although AI/ML do not magically solve these problems, they do enable models to reduce false alarms and identify patterns of fraudulent transactions that might be too subtle for humans to notice. In addition, AI/ML can dramatically reduce the list of fraudulent cases to be reviewed by human experts. The algorithms can correctly categorize a larger volume of the cases, before generating a list of “too close to call” cases for expert review. This gives consumers higher levels of security and financial safety.
Benefit #7: Optimizes credit risk evaluation
An AI predictive algorithm can return an immediate assessment of a user’s credit risk, allowing customer representatives to design a relevant offer. This technique increases the efficiency of offers by expediting the overall process of credit risk evaluation.
These are just 7 benefits of embracing AI/ML in business. When used properly, AI/ML can complement manual decision-making and human expertise. AI/ML will continue to impact business in the future. Ultimately, these technologies are less about replacing people and more about giving human workers technologies that help them do their jobs better, or more effectively.
“Predictive models consistently outperform human experts by 20-30%;
i.e. they make 20-30% fewer errors, or identify 20-30% more important or profitable cases.”
How to implement AI/ML in business
From choosing the right AI-powered tool to setting up the right data inputs and workflows, implementing AI can be cumbersome for companies. There needs to be an understanding of how AI works, the advantages it creates, and its various applications. To take your business to the next level using AI/ML, it can help to partner with an experienced specialist who understands how to develop and integrate a strategy that best suits your requirements.
● Artificial Intelligence and Machine Learning are sometimes used interchangeably, but they have different definitions. AI is an umbrella term for machines that can simulate human intelligence, while ML is a subset of AI.
● Predictive models powered by AI/ML can help businesses increase revenue by extending data analytics efforts to gain insights at a greater volume, quality, and speed.
● If you want to apply AI solutions in your company to enhance business processes, it can help to partner with an expert with in-depth knowledge and experience.
Like What You Read?
Encora’s team of ML and AI experts can help you leverage these technologies to improve or automate processes in order to improve revenue, reduce costs, or harness your product’s true potential. Together we can accelerate innovation and build a solid partnership to structure the financial landscape of the future. If you want to learn more about AI/ML in the financial services industry, download our free PDF ‘Encora’s Key Factors to FinTech Success’
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