The global machine learning (ML) market is expected to grow from $21.17 billion in 2022 to $209.91 billion by 2029, at a CAGR of 38.8% in the forecast period.
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Machine learning is one of the most common types of AI in development for business purposes today. Machine learning is primarily used to process large amounts of data quickly. These AIs are algorithms that appear to “learn” over time.
If you feed a machine-learning algorithm more data, its modelling should improve. Machine learning helps put vast troves of data – increasingly captured by connected devices and the Internet of Things – into a digestible context for humans.
For example, if you manage a manufacturing plant, your machinery is likely hooked up to the network. Connected devices feed a constant stream of data about functionality, production and more to a central location. Unfortunately, it’s too much data for a human to ever sift through; and even if they could, they would likely miss most of the patterns.
Machine learning can rapidly analzse the data as it comes in, identifying patterns and anomalies. If a machine in the manufacturing plant is working at a reduced capacity, a machine-learning algorithm can catch it and notify decision-makers that it’s time to dispatch a preventive maintenance team.
But machine learning is also a relatively broad category. The development of artificial neural networks – an interconnected web of artificial intelligence “nodes” – has given rise to what is known as deep learning.
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Machine learning helps put vast troves of data – increasingly captured by connected devices and the Internet of Things – into a digestible context for humans.
Deep learning is an even more specific version of machine learning that relies on neural networks to engage in what is known as nonlinear reasoning. Deep learning is critical to performing more advanced functions – such as fraud detection. It can do this by analyzing a wide range of factors at once.
For instance, for self-driving cars to work, several factors must be identified, analyzed, and responded to simultaneously. Deep learning algorithms help self-driving cars contextualize information picked up by their sensors, like the distance of other objects, the speed at which they are moving, and a prediction of where they will be in 5-10 seconds. This information is calculated at once to help a self-driving car decide when to change lanes.
Deep learning has a great deal of promise in the business and is likely to be used more often. Older machine-learning algorithms tend to plateau in their capability once a certain amount of data has been captured. Still, deep learning models continue to improve their performance as more data is received. This makes deep learning models far more scalable and detailed; you could even say deep learning models are more independent.
The survey below shows that advanced analytics was the most popular use case overall. This was closely followed by forecasting and fraud prevention. Although looking at the data through a less wide-angle lens, it is clear that company size can dramatically affect what techniques are implemented, with larger businesses focusing more on automating all aspects of the company to a degree.
Machine learning enables companies to achieve a wider variety of objectives than ever, from automation of manual tasks and increased operational efficiency to customer engagement and experience improvements.
1. Advanced analytics
Advanced analytics is the most popular use case for machine learning within fintech.
55.75% of surveyed companies indicated that they were working on advanced analytics.
It is an umbrella term for examining data, utilizing sophisticated techniques to uncover deep insights, produce recommendations, and form accurate predictions.
This method encompasses aspects that range from data mining, pattern recognition, and sentiment analysis to visualization and big data processing, demonstrating incredible scope for application in almost all elements within a business.
This trend of advanced analytics leading the pack does not waver due to the size of companies, with smaller companies holding it at 44%, with mid and large-sized companies sitting at 54% and 60%, respectively.
Utilising powerful algorithms to improve customer engagement and, most frequently, to predict consumer demand for products and services with an unparalleled degree of accuracy is essential. This is why so many companies rely on machine learning, no matter the size.
Forecasting sits at second place in the use cases for machine learning, with 44.30% of respondents saying it is within use in their organization.
Both small and medium-sized organizations have notably utilized this more heavily in their approaches, at an application rate of 42% and 46%, compared to large organizations. On the other hand, larger organizations have been seen to rely less heavily on forecasting; at 45%, it is still vital but demonstrated to be of less importance when compared to customer service and chatbots at 55% or fraud detection and prevention at 45%.
3. Fraud detection and prevention
According to McKinsey & Company, fraud in financial institutions is constantly evolving, and several trends fostered increasing diversity and complexity of scams in recent years.
Those trends are, for example, the shift to digital and mobile customer platforms, new solutions leading to payments transactions being executed more quickly and consequently, leaving banks with less time to identify and counteract potential frauds. For that reason, financial organizations try to use ML to adopt the changing landscape of fraud detection.
In the third position, with 38% of respondents saying they apply fraud detection and prevention techniques, this machine learning method is less widespread yet equally useful for organizations of any size. Through the use of well-trained models that learn from data patterns, this method allows one to identify and flag anomalies with ease; for example, in a bank’s transaction data, these flags would be suspicious operations on clients’ accounts.
Thus, a company can effectively protect itself and their customer data through fraud detection and prevention techniques without relying on manual flags or individual tip-offs. These methods are used less in smaller companies, at 31%. This drop in usage can be attributed to smaller data pools that still enable manual checking to be effective, meaning a company may not invest in technology they feel is unrequired.
Medium and larger companies are shown to rely more heavily on this form of machine learning, with 38% of medium companies considering it vital and an unsurprising 50% of larger companies agreeing on the same.
The larger data sets that come with increased company size will overwhelm a manual check system- and so we are seeing a rise in organisations turning to machine learning to fulfil this role in a way that meets both standards and demand.
4. Customer service and chatbots
Customer service chatbots are less popular machine learning methods, with 35.40% of companies utilizing them. These bots use artificial intelligence (AI) and machine learning to answer basic customer questions quickly. Chatbots can recognize and answer multiple forms of the same question using your preferred company voice and tone.
According to Gartner, AI chatbots are among the leading technologies and trends driving the digital workplace. AI assistants have a significant impact on how work gets done. Automation of repetitive tasks within customer service impacts not only the quality of customer service but also the employee’s workload.
Larger companies rely more heavily on AI chatbots at a 55% application rate than smaller companies at 33% and 25%.
This is because of how the chatbot is used within customer service. In a more significant business, chatbots provide a sense of ‘voice’ to the consumer. They allow customers to ask simple questions 24/7 without requiring a customer service employee to step in, giving a line of communication for the customer that isn’t a drain on company time or resources. This sense of ‘voice’ needs to be supplemented less in smaller companies as more physical staff will be available to answer questions and aid customers.
5. Onboarding customers/KYC
KYC involves identity verification of each new customer onboarded and continuous monitoring of them afterwards to identify any changes in company structure, which can benefit both owners and directors.
KYC is a less widely implemented method than previously discussed advanced analytics, with 33.60% of the companies we surveyed regularly employing it as a method.
By having a customer onboarding service supplemented with machine learning techniques such as product recommendation methods or integrated chatbots to help complete the transaction, both smaller and, large companies can turn visitors into repeat customers.
Small companies have implemented this similarly to larger companies, with a 33% and 35% application rate, respectively; these numbers could still climb if the correct information were provided to companies regarding tailored onboarding systems and how they could lift their business.
We as a team provide the below-mentioned services under machine learning:
When it comes to deep learning, we follow the below-mentioned services:
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