Digital banks face cyber, talent and model risks: Deloitte

Digital banks boast strong technological capabilities, yet uncertainties remain as to their ability to manage risks and maintain profitability, Deloitte said.

According to an analysis by the consultancy, the market size for unbanked and underbanked individuals and enterprises is estimated to be between US$55 billion and US$115 billion in Asia Pacific.

Regulators in the region have been particularly invested in helping financial technology grow and develop in their markets, and digital banks are part of larger programs to encourage innovation in the industry.

Leveraging their technologies, digital banks can obtain trusted data from their shareholders and partners, including customer profiles, behavioral data and sales records. Such data enables digital banks to identify customer needs and manage their risks, whilst improving productivity.

Nevertheless, while digital banks tout the advantages of their technology-first approach, uncertainties remain about the ability of these lean organizations to both manage risks and maintain profitability as regulators provide little leeway in meeting regulatory requirements.

Digital banks will have to contend with similar risks as traditional banks and they are particularly vulnerable to downtime because of their digital-only footprint. Properly managing various risks such as cybersecurity, privacy, data sharing and technology applications, as well as ensuring seamless customer experience and effective business continuity, will be critical to building trust with regulators and customers, Deloitte noted.

Besides, digital banks must assemble the right teams and get the team dynamics right. In Hong Kong, statistics showed 64 percent of fintech employers found the recruitment process difficult, with around half reporting that a shortage of proven skills was a key challenge.

Many digital banks will also have lean operating models with heavy reliance on artificial intelligence, machine learning and data analytics to automate processes and drive decision-making. While AI and machine-learning models may yield significant advantages for digital banks, they also expose banks to model risk. Failure of a model can lead to adverse outcomes, such as financial losses, operational lapses, regulatory breaches and reputational damage,

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