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finance

Banking on AI to Complete Financial Services

14 days ago by Andrea Amato
finance

Whilst a large sum of AI goes unregulated, banks can no longer shy away from popular technology.

In the past, financial services were somewhat hesitant to implement the newest technologies in their everyday processes. This intimidation is understandable, given the authority banks hold on managing the most personal of data for hundreds upon thousands of consumers. As technology is a rapidly advancing field, it's grown more sophisticated to manage security concerns with some training. One such technology financial services such as banking are adopting in their day-to-day operations is AI.

Whilst AI presents several use cases for finance jobs in Malta and beyond, it’s commonly used in risk management and revenue generation. Its popularity is also increasing, in that many financial institutions are exploring the ways AI can be used to solve increasingly complex circumstances. Now however, we mainly observe AI in minimal digitisation efforts: chatbots to handle consumer needs and in anti-payments fraud. Whilst managing consumer needs are important in banking, there are further applications of AI that can help transform financial services for the better.

Indeed, the intricacies of AI technologies including machine learning, natural language processing (NLPs), and so forth can assist back-office operations. With big data, for example, banks can save a lot of time and money on managing high volumes of transactions. The possibilities are vast, but financial institutions must remain cautious. At the end of the day, machine learning requires maintenance from humans to ensure performance is optimal. A dedicated team should exist to manage AI in terms of testing procedures to ensure continuous reliable outcomes.

With the above in mind, numerous resources exist to ensure banks decision-making related to AI is carefully considered. Any institution or service implementing AI should be wary of their structure so that employees understand and make use of novel tools for its inherent value and its assistance to drive better customer experiences.

 

Prioritising Customer Experiences

In many IT jobs in Malta and globally, including digital products available for banks to improve consumer interaction, are largely focused on ensuring users enjoy a pleasant experience when interacting with a service. With many services and products existing online, it’s difficult for financial services to stray away from amending their business models at least to some degree to support digitisation.

The consumer experience with banking has dramatically shifted: from visiting bank branches monthly to conducting transactions through an application daily, the connectivity between banks and consumers are constant.

Further, an internal shift within banks is due to the aforementioned novel operations that re-structure our understanding of financial services. Leaders and employers alike explore challenges in this space, for they must manage shifts in daily processes to support employees using new technologies. Today, the value of AI is appreciated among banks worldwide. They understand that AI can help drive better customer relationships and business experiences in amending their company model.

 

The Benefits & Challenges of Implementing AI in Banking

Whilst the benefits of using AI to improve banking systems and finance jobs generally are copiously researched nowadays, the following are not without limitations:

  • Time and cost-effective: whilst banking operations are typically digitised, a lot of paperwork is still involved in many financial institutions. With these increases the chance of human error in managing high volumes of documents. Robotic process automation (RPA) is one such way banks are overturning this issue; humans can set the rules for RPA to implement certain approaches to processes without the same time and error spent in completing these tasks.

  • Heightened customer experiences: as the main scope for banks to implement AI technologies, this is an important space for banks to tackle. Customer telephone lines are notoriously busy, so the use of chatbots that can work 24/7 to handle customer requests has been a game-changer for these institutions. Aside from managing consumer queries, chatbots can also present the latest offers and services of a bank.

  • Improved regulatory compliance: as computers work at tremendous speeds whilst handling high volumes of data, they are also able to quickly determine when fraud occurs and flag it to the consumer and banking system. Because of the personal data banks care for, a lot of regulations exist for these institutions to abide by and will suffer immense costs if they do not. The use of advanced algorithms has revolutionised this sphere, as AI can assist in monitoring transactions and user interactions, whilst abiding by regulations imposed by authorities.

Whilst the above use cases are more specific, there are general applications of AI that supports banking sectors. The automation of many banking jobs and processes allows for smarter decision-making for numerous operations, such as in decisions relating to investment and loans. As financial sectors begin to explore how they can utilise AI technologies to inform effective decision-making, there are several challenges to also consider.

One such challenge is in AI design. Contrary to popular belief that AI has a mind of its own, the ability for machines to learn an advanced algorithm that benefits organisations depend on the human that programmes it to begin with. There are certain biases that arise with this if not developed carefully, necessitating the need for leaders to examine AI models carefully. Even when a model is trained, it must be regularly updated according to novel needs that arise for the company, as well as external circumstances (e.g., chatbots to cater for Covid-19 consumer requests).

Secondly, as banks and similar industries operate under tight and lawful regulations, leaders and managers must understand how machines and their algorithms work to deduce decisions. This is because whatever action taken to fulfil some banking purpose must be explained to authorities, in that machines aren’t making decisions not evaluated beforehand by humans. This places further restrictions on using AI in implementing deep learning and neural networks, for these operate with minimal human involvement. Whilst there are less legal frameworks surrounding AI regulation in the financial sector, certain proposals are in place that will continue to adapt in meeting present applications in technology.

 

Final Thoughts

Digitising financial services, including banking systems have not always been popular. It’s only until recently we’re exploring how we can reap the most benefits without compromising legal regulations that surround data sensitivity. It’s for this reason that any organisation wishing to implement AI technologies in their everyday business models and finance jobs, should do so with caution and careful consideration based on current research and expertise.

Organisational structure is key in ensuring systems are in place: including a road map with prioritised strategies and approaches combining business objectives with AI, ensuring implemented systems are organised with dedicated tech teams, and applying technologies that have been thoroughly examined before implementation.

Nowadays, it’s difficult for financial services to disregard AI entirely. It’s up to leaders to carefully determine what can be implemented and if done so strategically, organisations can reap many rewards in both business and consumer satisfaction.