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Machine Learning Essentials
Machine learning has become an increasingly important part of modern life. It is based on using algorithm learning in a similar fashion to humans based on prior data. The goal of machine learning is to allow algorithms to make informed predictions without the direct input of a human programmer.
Machine learning can come in multiple forms depending on the goal of the algorithm. There is a spectrum of human involvement in machine learning that ranges from unsupervised learning to supervised learning. This variety allows developers to craft unique algorithms that are ideally suited to specific tasks and provide an unprecedented level of utility.
Impact of Machine Learning
The development of machine learning goes back decades, all the way back to the 1940s and the first proposals for algorithms that imitate the human ability to adapt and learn. Since then, numerous innovations have produced genuinely incredible examples of machine learning. Earlier innovations in machine learning primarily focused on analyzing data and cutting away unnecessary pieces of information. Some were developed specifically to learn the patterns of certain games, such as chess.
The utility of machine learning has expanded dramatically in recent years as software companies use machine learning to provide recommendations to customers based on their past activity on the platform. An excellent example of this is Netflix recommendations. Companies like Uber have even leveraged it for self-driving cars, and numerous other companies provide customer service chatbots that are empowered by machine learning.
The most newsworthy example is ChatGPT, which has been able to deliver accessible and unprecedented levels of machine learning capabilities. Unsurprisingly, many industries are realizing the potential of machine learning and are striving to find ways to leverage it for their own success.
Opportunities in Fintech for Machine Learning
Fintech has invested heavily in transforming financial services with machine learning. There are numerous uses for machine learning within fintech despite some challenges that can arise from its implementation.
Customer Service
As mentioned above, many companies have created chatbots empowered by AI to provide customer service, and financial services companies have heavily used this same solution. With machine learning, companies are able to offer faster customer service and free up their staff to focus on more complicated customer service requests. Additionally, this allows customers to access assistance at any time of day.
All of this translates into a better experience for customers and reduced costs for financial companies. This is especially true for financial services companies that have complex regulations or features that can cause confusion for customers and allow companies to provide customers with a new level of transparency.
Security and Compliance
Machine learning has been a game changer for fraud prevention. Its ability to quickly analyze massive amounts of data has made it much easier for financial companies to tackle fraud as soon as possible. Additionally, these algorithms are constantly learning, making it harder for fraudsters to break into financial systems.
Financial companies must adhere to a mountain of regulations, and it is often a considerable challenge to track and deal with each compliance issue. Machine learning steps in ideally by monitoring compliance and flagging any issues or analyzing compliance regulations and providing companies with operations recommendations that meet those regulations.
Human error for security and compliance is also dramatically reduced with machine learning. This results in enormous benefits for companies that are not facing hefty fines for failing to be compliant or becoming victims of fraud. New machine learning solutions provide a better experience and level of security that work to enhance customer trust in a financial services company.
Payments
Within the payment industry, machine learning is allowing financial services companies to expand their payment solutions like never before. AI is allowing payment to dramatically speed up their back-end processes. This results in lower prices and faster service for merchants that rely on their payment providers.
The ability of machine learning to prevent is tremendous for payments as chargebacks are incredibly costly. Now, with machine learning, payment processors and merchants can dramatically cut down on the number of chargebacks with excellent fraud prevention. Machine learning can also protect payment processors from risky merchants or fraudsters by conducting holistic risk assessments of merchant applications.
Future of Machine Learning in Fintech
Machine learning can be used to provide clear explanations of complex financial services. This serves two essential functions: providing clarity to regulators and making financial information more understandable for users. Together, these protect financial companies from fines and make their services more accessible to the average user.
Another way machine learning can dramatically improve accessibility in fintech is with chatbots that can provide service in multiple languages. Fluency in English has been a must in fintech, but new machine learning algorithms are making it easier for those who speak other languages to access fintech services.
Blockchain technology pairs really well with machine learning. This combination will allow more companies to decentralize financial solutions and provide yet another alternative to centralized financial institutions. Such a transformation in financial services increases security and efficiency while providing people with new avenues to engage in finance.
Machine Learning Tools with Under
Plaid
With Plaid financial services, companies can verify a potential lender or customer’s income with Plaid’s Bank Income tool. This tool uses machine learning to identify someone’s income based on their bank transactions. Additionally, you can locate income sources and types with Plaid and create an easy-to-understand analysis of a potential customer. This makes the underwriting process much smoother when underwriters can quickly and automatically check a potential customer’s income and whether that income comes with any risks.
Twilio
Twilio leverages machine learning with its Speech Recognition API, which is able to provide real-time translation for over 100 languages and dialects. This makes it much easier for customers to access services from Twilio as language barriers are taken down. Additionally, their software is designed to transcribe for users in all of these languages, and this allows people to speak and send in support requests faster than typing them out manually. These algorithms are also designed to learn the specific speech patterns of individuals and improve their responses with each interaction with a user.
iSoftPull
Companies benefit from machine learning by using iSoftPull in two essential ways. First, their decision optimization allows companies to streamline their supply chain and customer interactions with AI. Additionally, they provide machine learning-based fraud prevention and compliance software. Together, these enable companies to deliver better service and avoid costly fines or fraudulent activity. This is especially important for payment providers as iSoftPull’s machine learning is specifically designed to deal with payment fraud, which can be incredibly expensive for payment providers and merchants they work with.
Middesk
Middesk provides some incredible tools, and their industry classification tools are prime examples. It uses machine learning that analyzes a company’s online presence and reports what goods or services a business sells. This is especially useful for payment providers who need to be aware of merchants who are working in industries that can be classified as high-risk. Middesk’s machine learning is an excellent feature that reduces friction in customer onboarding, appropriately assesses risk, and prevents fraud.
Ekata
Ekata provides machine learning to allow businesses to verify customer identities more quickly and determine the risk level of specific customers. With Ekata, a company can automate much of their risk decision-making with automatic decision points that assess the potential risks of working with a certain company. This solution is focused on customer-driven analysis that can be adapted to better suit the changing realities of customer risk assessment. With Ekata, any company can dramatically improve their onboarding process and underwriting workflow.
Why Choose Under?
With Under, your business gets access to all of these incredible machine-learning tools. Rather than having to have several different logins or systems, Under allows you to leverage them all on one platform. Not only does this save your business money, but it also dramatically improves your underwriting and onboarding workflow.
Reducing customer-facing friction and providing sound underwriting is the core of what we do at Under. That is why we have solutions from all of these platforms integrated into our system: you deserve access to the best machine learning-based solutions. We understand that underwriting is an incredibly complex and often frustrating process, and our goal is to change that forever with innovative solutions such as machine learning.
Connect with us to learn more about how machine learning with Under can change the way you onboard customers. Machine learning is the path forward in financial services, and with Under, it is easier than ever to use machine learning to improve customer onboarding.
