The last few years have created a wave of innovation in the fields of payments and finance. With technologies like smart watches, self-driving cars and intelligent peripherals it’s difficult to imagine life without “plugging into” these innovations. But what are some of the risks associated with these new developments?
Artificial intelligence (AI) is increasingly being introduced into a variety of industries including payments and finance, creating risks such as bias or discrimination by AI tools. To mitigate these risks, AI developers can adopt a responsible design approach to AI-based products, which may require a shift in mindset for some.
Benefits Of Artificial Intelligence In Digital Payments
The payment industry is evolving and so are the methods by which we process digital transactions. Natural language processing is one such method that is becoming increasingly popular in the financial services sector.
This technology is perfect for processing digital payments since it can comprehend and translate human language. In fact, financial services organisations will spend more on AI in 2020 than any other industry, according to a research by IDC, at US$11 billion.
Digital transformation is inevitable for payments companies. The payments industry is under pressure to legitimate every transaction and keep user accounts safe.
In order to stay competitive, payments companies must embrace digital transformation. This will enable them to provide a better user experience and keep up with the latest trends in the payments industry.
1. Automating Data Collection, Processing And Analysis
Using artificial intelligence for digital payments can help improve the efficiency of data collection, processing and analysis. AI tools are being deployed by companies to collect and process customer data from both internal sources such as social media posts and emails as well as external sources such as company websites.
AI is also being used to cross-reference this information with publicly available data sets. By analyzing large sets of consumer payment characteristics (like transaction frequency and product preferences) companies can better serve consumer needs.
2. Reducing Fraud
Using artificial intelligence for digital payments also can help improve the efficiency of fraud detection. For example, there’s increasing use of AI to detect whether a transaction behavior matches a user’s identity profile by looking for patterns like the amount of time between each transaction being made.
AI tools have also been used to identify and flag fraudulent transactions that are made first on the card, then on another card or at another merchant altogether.
3. Improving Customer Experience
Companies can use AI to create personalized experiences online and in-store by analyzing different customer groups (like high-volume users, repeat customers or new customers) with various products/services based on their purchase history and preferences.
AI can be used to increase customer loyalty and engagement by making personalized product suggestions to customers.
For example, an AI-based digital assistant can help create a more personalized shopping experience for customers – suggesting items based on past transactions, payment history and other personally identifying information.
4. Increasing Security Measures
By 2030, AI is expected to boost the GDP of the financial and professional services sector in North America by as much as 10%, thanks to increases in both productivity and consumption.
Using artificial intelligence for payments also helps companies improve their security measures. AI tools have been able to detect fraud perpetrators using stolen credit cards or counterfeit cards by quickly identifying discrepancies in payment patterns – such as time-stamped transaction data that doesn’t correlate with a user’s typical purchase behavior.
AI tools can also be used to monitor transactions in real-time with the goal of preventing fraud before it occurs. AI tools are also being used to detect potential exploitation of data breaches and prevent account takeovers, as well as to combat social engineering.
5. Improving Customer Loyalty
AI can also help companies improve customer loyalty with AI tools. For example, comparing customers’ purchase behaviors across different stores or different merchants can help companies identify new ways of serving their customers and increase the likelihood that they’ll make repeat purchases with the same merchant.
AI tools are also being used to lock-in customers by predicting what they’re likely to need next. This type of predictive analytics can be used to recommend products or services that will likely appeal to a customer’s needs, such as what types of items they purchase most often and what other products they may want to buy.
6. Increasing Customer Optimization
In 2017, 84 percent, or more than eight in ten payments divisions, reported employing AI. By using real-time data from consumer transactions to improve future predictions, AI may also help businesses find ways to enhance the customer experience.
For example, an AI system can analyze customer purchase activity and make predictions about what products or services the customers might want to purchase in the coming weeks or months.
Similarly, an AI tool can analyze transaction data to predict how many transactions a merchant is likely to make over the next few months (or even years) based on its current rate of transaction volume.
7. Increasing Data Collection
AI can also help companies increase their data collection efforts. For example, by using AI tools to measure customer engagement and loyalty they can get more insight into what motivates customers to purchase certain products.
AI can also be used to help companies identify opportunities for new advertising campaigns or other types of personalized marketing. Companies are typically reaching out to customers through email and social media, but many people have abandoned these channels as a result of the volume and frequency of communication required by marketers.
AI tools can improve the effectiveness of marketing communications by identifying which messages are most likely to appeal to customers – as opposed to using broad-based messaging that may not resonate with every customer who receives it.
8. Improving Evaluation Of Company Performance
AI can also help these companies evaluate the performance of their own company by identifying areas where they’d like to improve and developing ways to accurately measure the impact of certain products or services.
AI can be used to track customer preferences and behaviors based on their transaction data. Using this data it is possible to gauge customer satisfaction and identify companies that are most successful at improving their customers’ experience.
That was 44 percent more popular than banking and accounting, the third most popular area, and over 20 points more popular than the second most popular sector, IT.
For example, using machine learning algorithms an AI system can identify patterns in customers’ behavior to determine which aspects a given product or service are most positively impacting customers. This feedback can then be used by organizations in the future to improve these aspects as well as other areas.
Risks Of Artificial Intelligence In Digital Payments
As more and more customers’ accounts are being false card declines, banks are taking a closer look at consumer behavior. Data gathered from card transactions show that, in many cases, the customer is actually using the card at the time of the decline.
This suggests that there is a problem with the bank’s system, not the customer’s account.
The global AI market is anticipated to increase by more than 150 percent this year and will continue to do so, with forecasts predicting that it would rise by 127 percent annually by 2025.
1. No Control
Most of the risks in applying artificial intelligence to payments are related to the risk of losing control over data. The main concern is that an entity may use a system to learn more about customers and their spending habits, including their card-transaction histories and expected future spending.
This is especially concerning due to the recent massive growth in data breaches and cybersecurity threats. For example, a cybercriminal could develop AI systems capable of predicting when consumers will make purchases so they can target them for fraud or theft based on assumptions about their usual payment behavior.
Several banks have started using AI tools for fraud detection, but if cybercriminals are able to hack these systems they could be able run amok with this information.
2. Untested Technology
According to Visa, counterfeit card fraud at brick-and-mortar businesses has decreased by 80% since the introduction of EMV cards, or the “chip card.” Worldwide card fraud on card-based transactions decreased overall in 2018 compared to 2017.
Not only will a company have to invest time and money into developing and deploying the system, but it will also need to hire additional staff with necessary skill sets if it needs specific updates or alterations.
For example, if a company wants to address a problem using artificial intelligence technology – such as fraud prevention – it may be best to enlist third-party experts who have developed similar systems.
3. Higher Expenses
In addition to the costs associated with developing and deploying an AI system, there is also the cost of testing and maintaining it. Many companies may be more comfortable with a more familiar technology that can be managed more effectively than a new AI system.
These companies could choose to leverage existing technologies rather than develop an entirely new system to better address their specific needs. For example, if financial institutions have developed sophisticated systems for fraud detection or anti-money laundering checks that are actually working well – perhaps because they have been developed using artificial intelligence tools – there is little reason for them to invest in another method when they are already doing something that works for their industry.
4. Lack Of Information Availability
In order to make AI systems work properly, they will have to have extensive data on the users that they are designed to help. The more data that is available, the better the system will be able to provide targeted assistance.
However, it is not always possible for businesses to collect every piece of data about every customer that they can – this is especially true for small or medium-sized businesses who may not offer as many services or products as some larger companies.
This could lead companies to completely ignore customers who purchase a small subset of their products and services because it would be too expensive and difficult for them to collect information on these individuals. This is one of the main reasons why banks are increasingly relying on artificial intelligence to lower fraud rates.
5. Privacy Risks
The other problem with using artificial intelligence systems to handle sensitive customer data is that these systems may not be entirely trustworthy.
It is difficult for a human to understand how an AI system works, so if a hacker were able to compromise the AI system in order to gain access to data for malicious purposes – such as fraud or money laundering – companies would find it difficult to know how the attacker was able to gain access and affect the system.
6. Potential Breach Of Banking Regulations
In addition, banks and other financial institutions are forbidden by regulatory agencies from storing certain types of personally identifiable information (PII), such as payment histories and card details, as well as business information regarding individual customers that banks have obtained from third parties.
Artificial intelligence is already being used to develop new payment methods, such as biometrics authentication and other digital identification services that will make it easier for consumers to transact without having to remember passwords or manage complex multi-factor authentication processes.
These methods are also poised to become more fraud-resistant than current methods, making it safer for both users and businesses to use digital payments.
Payment service providers will increasingly move away from cash and debit cards in favor of digital wallets and mobile payments systems that enable customers to use third-party applications instead of their own banks’ applications.