This systematic literature review examines the role of machine learning in fraud detection within digital banking, synthesizing evidence from 118 peer-reviewed studies and institutional reports. Following the PRISMA guidelines, the review applied a structured identification, screening, eligibility, and inclusion process to ensure methodological rigor and transparency. The findings reveal that supervised learning methods, such as decision trees, logistic regression, and support vector machines, remain the dominant paradigm due to their interpretability and established performance, while unsupervised anomaly detection approaches are increasingly adopted to address novel fraud patterns in highly imbalanced datasets. Deep learning architectures, particularly recurrent and convolutional neural networks, have emerged as transformative tools capable of modeling sequential transaction data and detecting complex fraud typologies, though challenges of interpretability and real-time deployment persist. Hybrid models that combine supervised, unsupervised, and deep learning strategies demonstrate superior adaptability and detection accuracy, highlighting their potential as convergent solutions.

ChatGPT is causing waves in just about every industry, and there's no doubt it will be a powerful tool in assisting roles that involve written and verbal communication. But will it replace a universal associate? Compare it to digital/mobile banking, and the benefits and drawbacks of the technology. While ChatGPT presents the possibility of efficiency and scaling, and there are security concerns and it's never going to completely replace your staff members and human interaction and unique problem-solving skills they bring to the table. Digital banking and ChatGPT are solutions for scaling.

We may think of Artificial Intelligence as some obscure, futuristic concept of the Fourth Industrial Revolution that is associated with scenes from "out-there" sci-fi movies like Star Wars and Terminator. But AI is already here and being used in a wide variety of technological developments that make our daily responsibilities more efficient and convenient. In fact, scientists believe we are already'cyborgs' because we already have a'digital self', which lives into perpetuity. Artificial intelligence (AI) is intelligence demonstrated by machines and contrasts with natural intelligence displayed by humans. Towards Data Science says "Artificial Intelligence is the ability of a computer program to learn and think. Everything can be considered Artificial intelligence if it involves a program doing something that we would normally think would rely on the intelligence of a human."

Digital banking continues to grow in momentum and importance. Jacquie Hersch, head of industry marketing for financial services at ServiceNow, joins us to discuss digital transformation and how banks can offer even more effective digital products and services. Below is a full transcript of my interview with Jacquie Hersch. Welcome to the BAI Banking Strategies podcast. I've been looking forward to it. Jacquie, ServiceNow recently collaborated Aite-Novarica on a white paper titled "Transforming the Banking Experience Through Digital Growth."

As the sophistication of artificial intelligence and intelligent algorithm technologies has increased, they now have the potential to revolutionize traditional banking models and deliver a shift to digital banking which is faster, more agile, and more customer centric. AI has the potential to transform all aspects of banking – from the way we save to the way we invest and spend – making possible a model of banking that is smarter, faster and more customer friendly. Artificial intelligence (AI) is one the modern world's most rapidly advancing technologies. Analysts predict that global investment in AI is set to hit $98 billion by 2023. In the financial services sector, we are seeing the huge impact AI and intelligent algorithms can have on the way we live and work.

For global banking, artificial intelligence (AI) could potentially deliver up to US$1 trillion of additional value each year, boosting revenues through increased personalization of services, lowering costs through efficiencies, and uncovering new and previously unrealized opportunities through the use of data, says McKinsey & Company. In a post titled AI-bank of the future: Can banks meet the AI challenge?, McKinsey says that banks have continuously adapted to the latest technology innovations throughout the years, and as the industry heads towards the AI-powered digital age, incumbents must adopt AI at scale and become so-called "AI-first banks." Several trends are accelerating banks' transition towards becoming AI-first, it says, with the first one being customers' rapid adoption of digital banking. COVID-19 has further boosted the adoption of digital banking with use of online and mobile banking channels surging an estimated 20 to 50% in the first few months of the pandemic. The emergence of digital ecosystems and so-called "super apps" is also changing the way consumers discover, evaluate and purchase banking products and services, it says.

Digital banking is all about making customer experiences faster, easier and more engaging. Voice response marketing through smart speakers and digital assistants is increasingly the go-to bank marketing channel for "on-the-go-go-go" consumers. Make it frictionless To access financial and account information, respond to calls to action and offers, register reviews, handle transactions or reach out for help, voice response marketing is gaining in popularity. Instead of having to type in a website URL, focus on a QR code, or click on a text link, all of which can cause respondents to procrastinate, the frictionless availability of smart speakers and digital assistants encourages in-the-moment action. That can make the difference between closing a sale and losing the prospect altogether.

Social trading is an alternative way of trading by looking at what other traders are doing and comparing and copying their techniques and strategies. Social trading allows traders to trade online with the help of others and some have claimed shortens the learning curve from novice to experienced trader. By copying trades, traders can learn which strategies work and which do not work. Social trading is used to do speculation; in the moral context speculative practices are considered negatively and to be avoided by each individual who conversely should maintain a long term horizon avoiding any types of short term speculation. For instance, if you look at the eToro, one the biggest Social Trading Platform.

Social trading is an alternative way of trading by looking at what other traders are doing and comparing and copying their techniques and strategies. Social trading allows traders to trade online with the help of others and some have claimed shortens the learning curve from novice to experienced trader. By copying trades, traders can learn which strategies work and which do not work. Social trading is used to do speculation; in the moral context speculative practices are considered negatively and to be avoided by each individual who conversely should maintain a long term horizon avoiding any types of short term speculation. For instance, if you look at the eToro, one the biggest Social Trading Platform.

This article was originally published in the Disruption Banking magazine, issue no 4 'Focus on AI.' Banking, like every other industry, is feeling the pressure to adjust to the age of instant gratification. This need for immediate delivery and satisfaction is pushing out old systems and practices to replace them with new banking technology that can respond to the needs of perpetually impatient customers. But how fast is this happening? It is difficult for such a traditional, heavily-regulated industry to move fast enough because it's weighed down by years of legacy technology. We've seen this many times over working on digital transformation projects with banks in the region.