The FinTech industry is no stranger to the usage of artificial intelligence (AI). We’ve seen AI being used in many InsurTech companies to analyze a person’s risk. We’ve also seen it in the lending processes to provide more accuracy to the assessment of a customer – but how is it evolving?
As more companies are utilizing AI, we’re seeing bigger and more complicated data sets every day that hold a consumer’s personal and private information. This has become a growing concern for consumers and regulators alike, risking their privacy and safety. There will be more regulations to protect the consumer from the use of personal information that could lead to bias. Companies looking to incorporate AI into their business models should consider what services are most necessary, and how they can avoid problems in the future.
Below, we’ll be discussing how the world of Artificial Intelligence is rapidly evolving in the FinTech industry, the potential risks involved, what concerns are most important to regulators and consumers, how cybersecurity AI will be able to combat the ever-changing market, and what are the newest ways companies are utilizing AI on their platforms.
AI & Machine Learning Technologies
Artificial Intelligence (AI)
A field that combines computer science and robust datasets to enable problem-solving.
Machine Learning (ML)
The utilization of computer systems that learn and adapt without explicit instructions, using algorithms and statistical models to analyze and draw inferences from the patterns made in data.
Although sometimes used synonymously, Artificial Intelligence encompasses the idea of a machine trying to mimic human intelligence whereas Machine Learning does not. It aims to teach a machine how to perform a specific task and provide accurate results by identifying patterns.
The two are often used together to bring great benefits to organizations. As data grows in both amount and complexity, these automated systems have helped companies to automate tasks and generate insight to achieve better incomes.
How is it used in FinTech?
As the FinTech space diversifies, we’re seeing new and creative ways companies are using AI and ML in their business models. Many have found it to be a helpful tool to provide a fair and unbiased service to any potential client. This could be in reference to receiving a quote for a loan, expediting an underwriting process, or even personalizing a financial service experience to better fit needs.
3 applications of AI & ML technology being used currently:
Insurance
Insurance policies is one of the most common ways AI is being used in FinTech. Companies have been able to calculate someone’s level of risk through their activity in a FinTech app. Assessing customers like this has opened possibilities for people to be calculated on their risk levels in a mobile app. AI can also be used to assist companies in handling email exchanges and ensuring all documents for underwriting are collected.
Improvement of operational efficiency can help companies to preserve the integrity and security of data and operations. This will give consumers a better quality of services than their providers overall.
Loans
Loans have been another popular way that FinTech companies are utilizing artificial intelligence and machine learning in their work. The use of these has helped reduce the number of inefficiencies throughout the loan process. There’s now more accuracy in the underwriting process through an improved client risk profile approach.
The loan process is a difficult one when using AI and ML, for it reduces the amount of bias that happens in human decision-making. Although it can help qualify people for loans that aren’t traditionally accepted, it can also do the opposite. If done incorrectly, companies risk segregating an important pool of users from being able to utilize their services.
Personalization
FinTech companies are using AI and ML together to create personalization through their applications. As we’ve seen, more users want the ability to personalize their finance experiences. Bringing their clients:
- Customized products
- Adapted content to fit their needs
- Binding communication channels
- Targeted advertising
Now, clients of banks independently solve financial issues thanks to digital banking and a personalized approach. According to Finextra, 79% of Millennials, 75% of Gen Zers, 74% of Gen Xers, and 58% of Boomers value personalization and consider it when choosing a new bank.
The use of AI in data privacy
As more services turn to the use of artificial intelligence and machine learning in their business practices, a main concern for consumers is their data privacy. The amount of data doubles every two years, generating a quintillion byte of data each day. As more people are using their smart devices to collect and transmit data through high-speed global networks, machine learning will accelerate the sophistication of AI.
We’re seeing artificial intelligence and machine learning grow in FinTech more than ever before. As it evolves, the ability to use personal information that may intrude on privacy interests has significantly grown.
Privacy issues in AI
Use of personal information
When talking about the privacy aspect in AI, there are several limitations and failures that are associated with these systems. Evaluating the effect of AI on privacy, machine learning technologies must distinguish between data issues that are relevant to all AI. This includes false positives and negatives, overfitting to patterns, and those that are specific to the use of personal information.
AI can have an algorithmic bias, causing there to be potentially unlawful or undesired discrimination in decision-making processes. Many financial technology companies are at risk of inadvertently discriminating against groups of people through their algorithms.
Discrimination is based on a person’s personal attributes such as their skin color, sexual identity, or national origin. These areas of personal information in automated decision-making that are against the interests of the individual involved implicate privacy interests in controlling how information is used.
Privacy regulations
As many concerns rise about the use of personal information in artificial intelligence, many governments are taking action to put in place privacy regulations. Rapid implementation has led to greater scrutiny of its risks, notably regarding discrimination and social media harms. Many states have attempted to attack this in several ways.
Colorado, Connecticut, and Virginia will require businesses to let individuals opt out of having their personal data used for automated decisions. California and Colorado require businesses to explain their automated decision-making logic to individuals.
These regulations have also gone international as well, with countries like Brazil, China, and South Africa, each granting individuals some form of redress, such as the right to contest or otherwise obtain human review of an automated decision.
Machine Learning in data privacy
There are growing concerns that machine learning models can’t make accurate forecasts unless they have massive data sets available. Including extensive personal and private information, it raises a big concern when it comes to consumer security and privacy.
Although no single technology will completely solve the privacy issue in the advancement of artificial intelligence, using encryptions within machine learning technologies could be the answer for preserving data privacy.
What to consider
AI and ML are relatively in their infancy, and just starting to break boundaries that require legal actions. We’re expected to see an uptick in the coming years. When you’re looking to either develop, deploy, or use AI systems, make sure you have done the following:
Know Your AI
Assess how often you are currently reliant on AI technology, and what you might need in the future. This is important to be able to answer all the questions management, boards, and regulators may have.
Lay Groundwork for AI Adoption
Knowing what kind of artificial intelligence your business needs is important for what risk(s) you should be aware of. Companies should be crafting policies to determine how AI is used in their organization. This monitoring can ensure minimal unfair outcomes.
Communicate Intentions
When integrating automated intelligence into a business model, companies should be able to respond to regulatory and consumer inquiries. A company that relies on third-party software should ask vendors and service providers for documentation for the underlying models that power the software’s systems. Your company should maintain records which demonstrate that the systems will not lead to disparate outcomes.
Risk Assessments
Risk Assessment should be a high priority when implementing new AI systems in a company. Through the datasets implemented for machine learning AI, risks are identified, companies should determine appropriate risk controls and start building controls into business units where risks can occur. Risk assessments may well become a standard and expected part of employing AI in organizations and will help companies do the legwork necessary to prepare to communicate about their AI.
Ongoing Regulations
The use of artificial intelligence changes over time, and what impact AI delivers may also increase. As these changes occur, there could be a bigger need for implementing ongoing, end-to-end governance structures for AI. Companies that have established information security programs, privacy compliance programs, risk management programs, or similar compliance programs could consider applying these structures.
AI in consumer protection
The Federal Trade Commission (FTC) has made a huge effort to combat online problems and urging policy makers to exercise caution when using AI as a policy solution.
AI-based systems can also be used to strengthen consumer rights. AI based personalization can help to ensure that information given to consumers as well as contracts are tailored to the wishes and needs of the individual consumer.
AI in cybersecurity
Cyberattacks have significantly grown in volume and complexity, and artificial intelligence is already helping security operations get ahead of potential threats. This has helped teams not only reduce risk but improve their security posture efficiently and effectively. Machine learning uses algorithms of previous datasets that make assumptions about a computer’s behavior. The computer will then adjust its actions to better protect it from potential threats.
AI and ML are quickly becoming critical technologies in information security, with the ability to analyze millions of events and identify different forms of threats. These can range anywhere from analyzing data sets to tracking behavior that could result in a phishing attack.
Why does cybersecurity need AI?
Artificial intelligence has been a helpful tool in all aspects of a business – cybersecurity is no different. Machine Learning and AI can help automate threat detections opposed to traditional software-driven approaches. However, there are some challenges that come with cybersecurity…
- Having a big attack surface
- 10s or 100s of thousands of devices per organization
- Hundreds of attack vectors
- Big drop in the number of skilled security professionals
- Masses of data beyond a human-scale problem
Machine Learning in Cyber Security is crucial to the adoption of AI. Comprehensive technology uses algorithms and datasets to make assumptions about a user’s behavior. The computer then can adapt and recognize potential threats.
Implementing a machine learning AI system helps businesses minimize these challenges. These AI systems will learn to gather data from all company information systems. That data is then analyzed and used to perform correlation of patterns across millions to billions of signals.
Advantages of AI in Cyber Security
Detecting New Threats
Artificial Intelligence can be used to spot cyber threats or malicious activities. Many of the traditional software systems are unable to keep up with constantly updated malware – this is where AI is beneficial.
Sophisticated algorithms used in AI systems are trained to detect malware, run patterned recognition, and detect any sort of behavior that would lead to ransomware attacks before it enters a company’s system.
AI-based cybersecurity systems provide the latest knowledge of global and industry-specific dangers to better formulate vital prioritization decisions. This is based not only on what could be used to attack your systems but also what’s most likely to be used to attack your systems.
Scalability and Cost Savings
Implementing an AI system can help to automate otherwise tedious security tasks, giving valuable resources the ability to focus on other areas of business.
Almost instantaneous threat identification of AI helps to reduce response times to security incidents, lowering the cost of defending potential threats.
Battling Bots
We see bots everywhere on the internet these days, and some pose major threats to companies. Automated threats cannot be beaten with manual responses alone.
Artificial intelligence and machine learning help build a thorough understanding of website traffic and distinguish between good bots (like search engine crawlers), bad bots, and humans. Teams can analyze a vast amount of data, adapting strategy to a constantly changing landscape.
Breach Risk Prediction
AI-based systems can predict how and where you are most likely to be compromised so that you’re able to focus resources towards areas of most vulnerabilities.
Insights from AI-based analysis enable you to configure and improve controls and processes to reinforce your resilience against cyber-attacks.
Better Endpoint Protection
As more companies work from home, more technology is being used. AI plays a critical role in securing these end points. Machine learning will better predict patterns of behavior. This means that to stay protected against the latest threats, it becomes necessary to keep up with signature definitions.
Disadvantages of Artificial Intelligence in Cyber Security
Although there are several advantages to implementing AI into your cybersecurity team, there are also some potential downsides. When building and maintaining an AI system, organizations need substantially more resources and financial investments.
Many of the data sets, malware codes, non-malicious codes, and anomalies needed to train an AI system are also time and labor-intensive. Without this, many AI systems will give incorrect results or false positives which could backfire.
Cybercriminals can also use AI to analyze malware, and even launch more advanced attacks. As your company grows, the amount of risk will increase, making an AI system almost necessary.
What to Consider
AI and ML are very helpful to a company’s cyber security process. It’s important to find the right balance to use these tools responsibly and effectively. Consider what processes they would be best for and what would benefit from human interaction.
Take into consideration…
- Data Quality: The quality of your data is a major factor in how your AI solution performs. Make sure you have a clean and well-annotated dataset to efficiently train your model.
- Model Selection: Identify what problems you’re looking to solve, the amount of data you have, and the desired accuracy levels.
- Security and Privacy: Consider the security and privacy of the data used for the model, and if the security system itself is up to code.
- Scalability: As your company grows, so will your number of users and volume of data. Make sure your system will be able to scale with the growth of your company.
- Ethical Implications: We’ve seen that it’s possible AI solutions risk showing bias, consider the ethical implications of your systems and if they are promoting fairness.
- Maintenance: AI systems will require regular maintenance and updates. Evaluate your plan to stay up to date with your system and ensure it’s running smoothly. Consider how you monitor the system’s performance and how you will troubleshoot/fix issues if they arise.
New & developing Artificial Intelligent-Specific regulations
We’ve already seen some very interesting stories about artificial intelligence and machine learning usage in FinTech, and more to come this year. In 2022, the market size rose to $865 million. By 2032, the generative AI in FinTech market size is expected to exceed $6.2 billion.
Here are some stories we’re currently following.
FinTech Stripe Integrating AI
The FinTech company Stripe has announced they’re starting to integrate OpenAI’s GPT-4 artificial intelligence model into its digital payment processing products. This move is said to help Stripe’s software developers to type out a question and receive summarized answers instead of having to search through developer documentation.
OpenAI will also utilize Stripe’s payment processing engine so they’re able to charge its users for subscriptions to their products such as ChatGPT.
Industry’s First AI Investment Assistant
TigerGPT, a text-generating AI chatbot introduced the first AI investment assistant in the industry. They now offer market and stock data, conduct investor education, and deliver deep analysis from various sources in seconds, empowering users to make efficient and informed investment decisions.
Leveraging AI for Compliance Purposes
In a time where AI is revolutionizing everything, we’re seeing an increasing need and desire to automate compliance. The introduction of ChatGPT created some waves within the FinTech industry, and as new regulations are being tightened, we’re seeing how AI is playing a big role. With new regulatory demands, technology is enabling the compliance function to sit as the central nervous system of an organization by using ML to mine data that can drive business decisions in multiple functional areas.
What’s Next for AI in FinTech?
Artificial Intelligence is in an interesting spot right now, as we see its use more frequently in different industries. As we see AI and ML diversify in FinTech, the current attraction has mainly been for insurance purposes or to keep up with current compliance regulations. Machine learning technology has helped FinTech companies manage the ongoing restrictions within the sector, and we can expect this to play out in the coming years.
The more AI can progress, we expect to see it for more niche areas of FinTech. AI Micropayments – a system that can charge miniscule micro payment amounts in fractions of a cent based on usage – could be one area that sees a lot of growth this year.
Anticipate this sector growing significantly in the years to come, and how this will be affecting your company’s growth over time. We’ve seen AI become a crucial part of FinTech businesses and is only expected to grow. Take this time to gather information on what needs your FinTech has, and how AI will bolster these rapid changes.
Your Partner in Growth
As the FinTech industry continues to grow, so does the need for talent to facilitate this. At Storm2 we have specialized in connecting FinTech talent with disruptive FinTech players such as yourself. We can assist in any stage of your growth by connecting you with the right people. Please don’t hesitate to get in touch and we would be more than happy to see how we can help and support you on your journey.
