A digital-first movement has been taking hold of the marketplace with mobile technology taking over almost all sectors and banking is no exception to it. Mobile banking isn’t a new technology. The number of smartphone users is increasing at a fast pace and with it, mobile banking has expanded, making the customer-bank relationships a challenge.
In addition, the introduction of artificial intelligence (AI) has further revolutionized the banking industry. While the area of execution was hard-coded with rules in traditional software, AI-based banking apps can now create their own rules based on the guidelines and data provided to them. This allows mobile banking to move a step further from rule-based to logical thinking and reasoning. Rapid innovations in this space mark the evolution of this technology on the mobile banking sector.
What is Artificial Intelligence?
You may be wondering why AI is so appealing, especially to the banking industry? What makes it so much better than human intelligence? The reason is simple. The purpose of technology has always been to minimize human efforts and automate task management. We’ve seen this concept in action over the years, with a multitude of industrial systems having been computerized for organizing and processing information using a comprehensive program structure. Moreover, AI processes real-time information related to human actions and behaviour that offers rapid intelligence, which arguably makes it as good and as credible as human intelligence.
The different adaptive intelligent capabilities of AI technology are constituted by:
- machine learning,
- machine vision,
- natural language processing, and
- knowledge-context management.
These capabilities mean AI can adapt over time to deliver accurate results, eliminate redundant processes, and even start predicting future actions or events.
The Mobile Banking Revolution
The availability of open-source software, data, cloud computing, and fast processing is the reason for the widespread adoption of AI in the mobile banking industry. This has revolutionized mobile banking in several ways and has proved to be beneficial for the banking sector as well as for the users. Some of these can be summarized as follows:
Enhanced Customer Experience
AI has not only automated the banking processes for the customers, but also provides them with a personalized experience. The processes have been highly simplified with the use of intelligent automation and repetitive processes can be automated to improve customer service via chatbots. The banking system earlier catered to large groups of customers by offering products that looked and felt similar, though the customers actually varied in their motivation, buying behaviours, and satisfaction levels. So, AI has helped to carve out customized services and experiences for individual customers.
A few areas where AI provides a personalized experience to customers are:
- help making financial decisions (based on the user location, situations, preferences, constraints, context, preferences, and recommendations),
- investment strategy tips (based on earnings, liabilities, investments, contingency, insurance, risk factors, monetary inflation, etc),
- notifications about latest products and services in the customer’s interest (based on their in-app behaviour or purchase history),
- guides to help users navigate through the app to products or channels they may be needing (based on user analytics and chatbot conversations), etc.
Efficient and Quicker Transaction
With the evolution of AI in mobile banking apps, transactions have become much faster and consume much fewer resources now. The user is quickly guided to their preferred channel using AI-based apps and boosts the efficiency and speed of transactions with automated, real-time payment processing.
By investing in AI, banks can effectively reduce the costs of hiring offshore or onshore employees, become more efficient, and also maintain strong customer service. This makes the whole process more cost-effective as it meets the growing demand for maintaining lean operations while delivering an exceptional experience to customers at a lower cost. The need for manual data entry has also been eliminated as transactions and bills can be automated.
Role of AI in Enhancing Next-Generation Mobile Banking Security
AI has played a huge role in personalized banking transactions and reducing costs for financial institutions but it also plays a big role in cybersecurity for the modern bank. With the right set of rules, AI can be used to keep your mobile banking customer’s money safe and secure. Here’s just a few areas where AI can really enhance security.
Users rely on banks for the secure execution of their transactions and thus; banks consider it their primary responsibility to secure the money of clients. Using AI in mobile banking apps, it is not only possible to detect fraudulent transactions based on a set of pre-defined rules, but suspicious activities can also be identified based on the transaction history and behaviour of individual customers. For example, if the transaction of a massively large amount is initiated from a bank account that has a history of minimal amount transactions or logins, AI can immediately withhold the transaction until it is verified by a human. Based on the data fed and the results learned from past actions, the AI-based software can perform such analysis in real time.
For banks, a major risk is involved while providing loans. Traditionally, banks used to perform a manual risk assessment process to estimate the creditworthiness of the borrower and this involved the historical data of the prospect, such as the credit history, transaction history, and estimated income growth. However; as historical data is not an accurate standard for predicting future behaviour, inconsistencies crept in. With the introduction of AI, it is possible to analyse real-time data of latest transactions, market conditions, and the recent news that can help in identifying potential risks in giving the credit. Banks can analyse even huge data sets to understand activities at the micro level using the predictive analytics of AI. This can also enable them to assess the behaviour of prospects for identifying a possible fraud.
Supervised Machine Learning
This is the most conspicuous analytic approach used today, mainly for siloed data. It is also known as “anomaly detection” and represents a logical point for entry. With the banking sector having large amounts of data, there is a need for anomaly detection to enhance security. However; as it triangulates anomalous behaviours across many data dimensions, there is still room for its growth and maturity. Once all these dimensions are covered, machine learning will enable the identification of the riskiest behaviour and also provide automated investigative context thereby eliminating unnecessary manual effort.
Choosing the Right Talent and Data for AI
Banks and high-tech giants need to identify AI experts for their business who can supervise the AI development process. Data needs to be managed and segregated properly to assess its quality and the need for applying AI to it. Analytic approaches to data can be successful only if data quality is addressed properly. This also helps focus on the data that needs security, rather than focusing on unnecessary data to manage risks.
Using AI in mobile banking will change the industry to an unimaginable extent – creating completely personalized and unprecedented customer experiences and helping to prevent fraud for both end customers and the banks themselves. However, with these comforts come security risks as well, which need to be considered before the services are released. Strong user authentication into the apps and advanced identification controls are just two of the main considerations developers and executives should have in mind to help keep AI a positive addition to mobile banking instead of a security risk.
Note: This blog article was written by a guest contributor for the purpose of offering a wider variety of content for our readers. The opinions expressed in this guest author article are solely those of the contributor and do not necessarily reflect those of GlobalSign.