The world has been undergoing a rapid technological change, advancing so fast that what was once deemed a science-fiction dream is now a critical part of our everyday lives. Artificial intelligence (AI) refers to a machine’s capability to carry out mental processes such as learning, logic, reasoning, problem-solving, perception, and creativity. Once believed to be exclusively human, these skills can now be replicated by technology and be applied in every industry (i.e health care, finance, automotive, etc). Over the past few decades, AI has achieved many technological breakthroughs, from playing checkers to generating its own content!
According to IBM Global AI Adoption Index 2022, AI adoption is growing steadily, with 35% of companies and organisations having already deployed them and 42% at the exploration stage. There is no doubt that AI will continue its explosive growth into 2023. Here are three AI trends to lookout for in the coming year.
1. Explainable AI (XAI)
Like human nature, bias also exists in AI. Bias in AI occurs when the machine produces results that are systematically prejudiced. Algorithmic bias exists due to it being trained and fed (consciously or unconsciously) data sets that are prejudicial towards certain demographics (i.e race, gender, nationality, age, class, etc). This is a major concern for many industries. For instance, in the financial sector, AI offers an opportunity to create a fairer and more inclusive financial system through alternative credit scoring. It is able to avoid the traditional credit scoring and reporting system, which helps to perpetuate existing bias in the industry, especially towards the unserved and underserved market. However, if the data set that is being trained and fed into the system is biassed in the first place, the AI will then produce algorithmic bias, causing it to amplify pre-existing biases.
Fortunately, the rapid advancement in tech has pushed companies to do more to combat algorithmic bias — which is through explainable AI (XAI). Gartner defines XAI as a set of capabilities that describe a model that highlights its strengths and weaknesses, predicts its likely behaviour, and identifies any potential biases. With XAI, machine learning (ML) models are no longer “black boxes” as they are able to provide an explanation of the decision made, thus creating AI models that are transparent, understandable, and allows room for course-correction. For example, XAI solutions in the lending landscape will be able to help lenders to not only explain to consumers why their loan applications were denied, but also to identify any subtle systemic bias in the model output and retrain them as necessary.
2. Adaptive AI
Next on the list is Adaptive AI. Flexibility and adaptability are the name of the game. The COVID-19 pandemic has taught us that there are bound to be unpredictable situations and the slightest disruption in the market or environment can cause drastic and significant impacts on many businesses and organisations. At the same time, decision-making is becoming increasingly connected, contextual, and continuous. This results in decision intelligence systems becoming more complex and will need to be reengineered to use adaptive AI so that it can exercise more autonomy when adapting to the changing environment.
Gartner defines adaptive AI systems as systems that support a decision-making framework centred around making faster decisions while remaining flexible and adaptable when new issues arise. These systems aim to continuously learn when being fed new data and adapt more quickly to changing real-world circumstances. In other words, adaptive AI can revise its code to incorporate what it has learned from new data and adjust accordingly even for changes that were not anticipated or known at the time the code was first written, all while maintaining accuracy. It is effectively learning as things are happening, and it can occur while in production or even in post-deployment. Hence, the deployed model can be used over the years without having to replace it.
Undeniably, adaptive AI will transform businesses across the globe. In the financial industry, adaptive AI will be able to help lenders to predict self-curing customers. Self-curing is a strategy that gives a grace period for customers to proactively repay their outstanding balance before using the organisation’s resources to contact them requesting for that repayment. Self-curing predictions are assisted by a set of predictive rules generated by an AI model. By incorporating adaptive AI, the model will continuously learn as it absorbs more data, allowing it to automatically update and improve itself whenever new data gets fed into it. This means that the model would have the flexibility to adapt to changing conditions and improve its prediction accuracy all the while maintaining the model’s relevance. For instance, if a recession were to occur, spiking the debt to income ratio, the model would still be able to adapt to the changing conditions to predict self-curing customers. Therefore, debt collectors can now strategise and focus on customers with higher probability of defaulting as technology now provides greater insight and management for self-curing accounts.
3. Generative AI
Over the past five years, venture capital firms have invested over $1.7 billion into generative AI solutions. Generative AI gained huge traction in 2022, and it will continue to do so in 2023. But what is generative AI?
According to Gartner, generative AI refers to AI techniques that learn a representation of artefacts from data and use it to create entirely new, original artefacts that preserve a likeness to the original data. The artefacts produced can take the forms of text, image, video, audio, and code, which could potentially be a major game-changer for many businesses. While generative AI is highly popular within the creative industry as it makes content creation much easier, but even so, its influence in the engineering field should not be disregarded. One of the greatest advantages of generative AI in code generation is improving developers’ productivity by providing suggested code. It is worth noting that the goal is not to eliminate human programmers, but to help them to improve their speed and effectiveness in coding while minimising bugs and errors. As proof, one Deloitte experiment showed that engineers using AI to generate computer codes has sped up development time by 20%.
AI has changed our world and it will continue to do so
AI is evolving at an unprecedented pace and will lay the groundwork for more technological innovations and business opportunities. As the technology matures, businesses are coming up with innovative ways to incorporate AI into their products and services, whether it is to stay relevant in the ever-changing environment or to set themselves apart from competitors. In 2023 and beyond, businesses and organisations will continue using AI to simplify complex work and executives should start preparing their companies for the AI-enabled future.
So here’s the million dollar question — how many of these AI technologies is your business planning to use in 2023?
JurisTech (Juris Technologies) is a leading Malaysian-based fintech company, specialising in enterprise-class software solutions for banks, financial institutions, and telecommunications companies in Malaysia, Southeast Asia, and beyond.
As one of the Fintech pioneers in Malaysia, our vision is to enable financial inclusion for the financial industry with our diverse range of solutions. Check out our latest AI-powered technology Juris Mindcraft, an explainable and adaptive AI that provides an explanation behind every decision reached, which helps banks and financial institutions to transform their digital landscape.