Payments Security Landscape The risk of information security breaches affecting data, application logic, and financial value is continually growing. For this reason, artificial intelligence and machine learning-based infosec tools for banking and mobile payments are getting huge attention. Starting from risk underwriting to credit rating and scoring to digital security and loan underwriting, there are a few strong contenders.

It aims at detecting anomalies, alerting, and blocking. Any abnormal pattern is a sign of worry. Sadly, we should not forget that hackers are also using artificial intelligence in cyberattacks that are more advanced and harder to detect. This blog post focuses on mobile financial services security and questions about digital payments.

Mobile Payments – Speed & Security

Against escalating security threats to mobile and digital payments, the need of the hour is to empower the whole ecosystem. The empowerment of merchants, acquirers, and service providers with new commerce opportunities backed by machine learning, edge analytics, and blockchain The customer experience in-store and data protection should be the highest priorities for any digital payment system. Banks may not be able to keep up with the need for speed as they lack the ‘culture or mindset’ of on-the-fly innovation and are thus not so open to new technologies. Secondly, money needs to follow consumers and not the other way around, like in traditional brick-and-mortar channels.

The Ministry of Innovation adds lots of excitement and innovation, thus increasing the fire about the security and privacy of transactions. Subscribers normally don’t ask too much in low-value transactions, but as it happens on a daily basis, subscribers do get nervous and freak out more when they add their card or bank details on provided channels, i.e., mobile apps, web portals, etc. Sadly, data privacy is still in the back seat for many providers.

As digital payments evolve, technology providers and financial institutions are working on ways to enhance security, privacy, and customer trust. Strategies that include adding new layers for fraud protection, such as tokenizations, where transactions can be completed without sharing sensitive data capsules (credit card number, CVV, expiration date, etc.).

Innovations in Banking, Payments and Customer Service

In the digital banking industry this year, the leaders who are creating state-of-the-art apps, chatbots, authentication, and internet-of-things applications will throw out what they do, but why and how answers may not come out. The issues will be debated, like how and with whom to share account data and whether or not to try to compete with top-rated mobile wallet apps. The brightest minds in the industry should lend a hand to share ideas, network, and collaborate.

Applying robotic process automation in banking, fintech, or any financial services business can bring significant savings and reduce negative impacts. Robotics is quickly gaining traction in banks to automate their everyday finance and risk processes. The Royal Bank of Scotland is rolling out a customer service “hybrid bot” from vendor LivePerson that hands over to a human colleague if questions flummox its artificial intelligence. With this tool, customers can message their day-to-day queries.

A variety of techniques exist for fortifying them in today’s time, but AI would be much more effective compared to the tools at hand.

  • Distributing query processing by combining hosted and on-premises DNS services, deploying recursive servers to the network edge, and creating redundant DNS architectures
  • Using response policy zones to cut off botnets and create whitelists for legitimate traffic
  • Rate-limiting noncompliant devices
  • Sharing threat intelligence to stay ahead of attackers and create a unified front

Arguably, the most destructive cyberattack is a distributed denial-of-service (DDoS) attack. Other attacks cause great harm—they steal computing power, exfiltrate sensitive information, and hold files and devices for ransom. But DDoS attacks are the brute destruction of critical services. As the Dyn attack demonstrated, they can extend far beyond single organizations. The use of AI-based (ML logic) application servers with logic that has been learned over time is essential, so they are a primary DDoS target—but they are preventable or defensible.

Mobile Payments Security Will Play a Key Role

In the case of digital payments, a payment velocity check is a key component to detecting and stopping fraudulent cases. If not checked, it can result in a brutal wave of chargebacks. Velocity detection might seem like some complicated tools to launch a space shuttle or tools to use at the theme park on a broken roller coaster. But in reality, velocity detection is defined as checking the historical shopping patterns of an individual and matching that record against their current purchases to detect if the number of orders by the cardholder matches up or if there appears to be an irregularity. Artificial neural networks are a big help in this particular space, and in the near future, they will be much stronger.

Mobile Payments #AILabPage

A successful attack on the software-based mobile payment application could consist of decompiling the source code, where the attacker obtains access to all assets hidden in the application (such as tokens and cryptographic keys). The integrity of an application can also be compromised by data tampering and cloned applications intercepting sensitive data. Another point of vulnerability is a merchant’s mobile POS, as a fraudulent merchant could tamper with the mobile application controlling the mobile POS. With these methods, an attacker can obtain assets such as user and card details, card verification method values, and keys. 

Security mechanisms, such as white-box cryptography, reduce the likelihood of cloning and decompiling payment applications. The provisioning of secure data to the SE or the delivery of a payment token is a point of vulnerability in mobile payment applications.

Emerging Technologies – AI, ML and DL

As per Sir Andrew NG – AI is the new electricity to power up any business of today with the ability to kill the business if ignored. Machine Learning and Deep learning are part of the AI domain as a subdomain.

  • Artificial Intelligence – An umbrella that gives synthetic thinking approach to all technologies take shade under this umbrella. AI solves problems in a heuristic way with being explicit or meta-heuristic.
  • Machine Learning Machine Learning is a subset of artificial intelligence where computer algorithms are used to autonomously learn from data and information. Machine Learning is; where business and experience meet emerging technology and decides to work together.
  • Deep Learning – Subset of Machine Learning. It is an algorithm that has no theoretical limitations of what it can learn; the more data you give and the more computational time you give, the better it is – Sir Geoffrey Hinton (Google).

Artificial intelligence is set to transform the financial services industry. How AI will be transforming the future of FinTech to elaborate items from the above list in African markets and opportunities are even more dramatic in just the past five years.

Digital Transformation – Payments Security Landscape

In today’s time, Digital Transformation without machine learning, data science and blockchain techniques is a kind of loud melodious whistle in an empty vessel. Lots of customer education, mindset change drive, as well as behaviour change, is needed. Financial capability is the internal capacity to act in one’s best financial interest, given socioeconomic environmental conditions. A few golden rules to get quick wins are as follows.

  • Needs to focus outside “digital and social media channel” i.e focus on radio, roadshow with village communities, focus on groups within local language and style
  • Trust local people to act as brand ambassadors for increasing customer loyalty and trust
  • Focus on creating a cost-effective and efficient operating model is the golden key
  • Carefully thought-through branch expansion versus setting up an agent network
  • Managing risk, security, compliance and bringing it up to the global standard
  • Leveraging mobile as a primary medium for transactions and queries and online banking
  • Technology-enabled customer engagement and continuous innovation
  • A complete set of counter-measures against Money Laundering and the financing of terrorism and proliferation, covering the required legal, regulatory and operational measures through and through knowledge set
  • In-depth knowledge & willingness to attain knowledge on principles for mobile financial services Infrastructures.
  • Understanding and willingness to attain in-depth knowledge and hands-on core banking platform integration with MFS systems, architecture, banking grade switching and rules around the same

AI may turn out destroyer of cybersecurity as well. For example, people who have succeeded in harnessing the power of artificial intelligence to create some sort of program. Combined with existing tools to figure out a quarter of the passwords from a set of more than 43 million profiles is a big breakthrough.

Points to Note:

We have covered all basics around mobile payment security and the importance of mobile payment data. AI is becoming a classifier instrument to put banks in good and best bank category. So banks that want to jump to the best category are jumping to adopt AI, BOTS and machine learning techniques. This is possible only after banks can utilise and understand the data they have. Data to serve and understand customers etc. All credits if any remains on the original contributor only.

Books + Other readings Referred

  • Research through open internet, news portals, white papers, notes made at knowledge sharing sessions and from live conferences & lectures.
  • Lab and hands-on experience of  @AILabPage (Self-taught learners group) members.

Feedback & Further Question

Do you have any questions about AI, Machine Learning, Data billing/charging, Data Science or Big Data Analytics? Leave a question in a comment section or ask via email. I will try best to answer it.

Machine Learning (ML) - Everything You Need To Know

Conclusion: There is clearly an opportunity for smart mobile/digital payments. Consumers want to pay quickly, easily and at low costs. An interesting finding is the need to add context to payments, e.g. subject or photo. Privacy and security are flagged as important by the majority of respondents. However, this was expected. With the knowledge of knowledge, we see more lean products focused on a specific group of customers.

The idea and concept are not new, however, it is very promising when targeting the right niche and addressing the right issues customers are facing. Now another type of AI which is going around like fire in a jungle; where it’s been said AI will stop all frauds and kill all issues around it. AI will bring behavioural biometrics to stop the gap and remove the vulnerability of payment systems, especially online payments.

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Posted by V Sharma

A Technology Specialist boasting 22+ years of exposure to Fintech, Insuretech, and Investtech with proficiency in Data Science, Advanced Analytics, AI (Machine Learning, Neural Networks, Deep Learning), and Blockchain (Trust Assessment, Tokenization, Digital Assets). Demonstrated effectiveness in Mobile Financial Services (Cross Border Remittances, Mobile Money, Mobile Banking, Payments), IT Service Management, Software Engineering, and Mobile Telecom (Mobile Data, Billing, Prepaid Charging Services). Proven success in launching start-ups and new business units - domestically and internationally - with hands-on exposure to engineering and business strategy. "A fervent Physics enthusiast with a self-proclaimed avocation for photography" in my spare time.

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