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Technology

The New Era of Banking: Embracing Digital Transformation and AI Innovations

iStock 1452503559 - Global Banking | Finance

The New Era of Banking: Embracing Digital Transformation and AI Innovations

Picture34545 - Global Banking | FinanceBy Iwona Sikora, SVP Europe & South Africa, Iron Mountain

In an era defined by rapid technological advances and an ever-evolving regulatory landscape, the need for banking institutions to remain both competitive and compliant has never been more critical. Much of this is driven from current economic headwinds and rising operational costs, which have created new pressures for banks.

In response to such challenges, the last few years have seen the industry witness exponential growth in two areas: ‘life-centric banking’ built around operationalising customer needs, and niche digital banks catering to specific demographics. More than anything, adapting and innovating in this transformative era is crucial for banks to overcome challenges and stay profitable. So, what’s fuelling this new era of transformation?

Harnessing the Power of Artificial Intelligence

Banks are increasingly leveraging the power of artificial intelligence (AI) to combat fraud, respond to evolving customer demands, and see off competition from smaller and more agile fintech rivals.  By 2025, the banking sector is set to spend an extra $31 billion on embedding Artificial Intelligence (AI) into existing systems. This alone shows us how indispensable AI technologies are becoming to the world of banking.

For instance, adopting advanced AI algorithms, large language models, and customer ML models -streamed through intelligent document processing solutions – will be crucial for helping banks better unlock ‘dark data’ and bring out value from unstructured data. They can uncover previously unrecognised data points, expose errors, enhance fraud detection, improve customer service, and customise offerings to meet specific customer needs.

Banks are integrating AI-driven document processing solutions into their fraud detection systems. By leveraging large language models and advanced machine learning models, the bank’s system automatically analyses transaction data, identifies suspicious patterns, and detects potential fraud in real-time. This improves the bank’s ability to protect against fraudulent activities, ensuring the security of both the bank and its customers.

Elsewhere, AI-powered chatbots and virtual assistants undergo rigorous testing to ensure fairness and mitigate bias. These intelligent conversational agents provide personalised and unbiased customer support, empowering the bank to deliver exceptional service to all customers while future-proofing their AI strategies.

But to successfully leverage any of these innovations, banks need to build a comprehensive, enterprise-wide data strategy. Here, more banks are embracing – this refers to decentralized data architecture that organizes data by specific business domain.  Rather than being stored centrally and owned by a single team, data ownership is distributed across various teams, each responsible for their own domain’s data. This makes the data more accessible and valuable across an organisation.

Capturing The Elusive Gen Z

Across the board consumer demands are changing, and banks’ priorities across consumer demographics are also shifting.

To that end, banks are acting swiftly. A number of traditional banks have begun embedding fintech services such as Banking as a Service (BaaS) which involves financial institutions opening their platforms to third-party providers, allowing them to offer banking services to customers. Whilst Buy Now, Pay Later platforms and similar solutions have continued to gain popularity with Gen Z consumers.

Embedded fintech signifies more than just a trend; it represents an age of integration and innovation.  But while this approach brings many benefits, it also poses significant risks, particularly around data security and consumer protection. The shift towards such platforms therefore demands a seamless transition that is also compliant.

Creating a data-driven strategy

Creating a data strategy involves finding use cases, addressing legacy constraints, and investing in capabilities to support current and future needs. For example, intelligent document processing and content service platforms can help banks extract valuable insights from vast customer and financial data. Further, automating and streamlining processes can reduce manual inputs, speed up processing, and provide a comprehensive view of operational insights through intuitive dashboards.

Partnering and integration is also key to success. Leveraging data is crucial for long-term competitiveness and requires agility and collaboration with third parties, empowering customers with tailored financial solutions. Working with specialists – whether a cloud or fintech provider, or a trusted technology partner – can help unlock the benefits of transformation even more efficiently, deliver a faster return on investment and help banks painlessly reach their desired business outcomes.

Banking regulation thresholds continue to change frequently – these can be complex to navigate and even more difficult to implement. However, to succeed in this changing landscape, banks will need to be prepared to adapt to new technologies and innovate their processes and offerings.

Pursuing a digital transformation shouldn’t merely be an option, but a business imperative. And above all, banks should be ready to embrace innovative data practices to empower customers in their financial journeys, now and in the future.

Ultimately, the need for stability and resilience in the current economic climate doesn’t have to be at odds with investment in innovation. Stability and working with trusted organisations will be guiding principles for all banks across the globe.

Data Mesh Explanation: While the Data Mesh concept is introduced, there’s an assumption that readers are familiar with it. A brief one-liner explanation could make it more accessible to a general audience.

In the section about Gen Z, the statement, “Once upon a time the older generation drove the trends and habits…” could be interpreted in various ways. Consider specifying which older generation is being referred to or rephrase to something like “Historically, older generations set the trends and habits…”

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