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A raft of obstacles face financial institutions, all with the potential to slow the growth of their business and block them from adopting digital solutions to produce efficiency gains. Siloed data, poor cybersecurity processes, the need to achieve and maintain recognised security certifications, growing costs, inflexible systems and ever-shifting regulatory compliance mandates all add unwanted pressure to the company’s systems.
An industry report from Bank of America found that the Millennial and Generation X age groups are most likely to use digital banking services, increasing the demand for modernised solutions.1 Unfortunately, many financial institutions have not adequately prepared for this push towards digitalisation. This was highlighted by a survey from Wolters Kluwer’s Finance, Risk & Reporting revealing approximately 90% of banks have major concerns about data management, while 42% of respondents noted their main challenge creating an integrated and consistent view of data across the business.2
Financial services firms’ efforts to digitally transform their organisations are hampered by an increasingly complex data management landscape. In many cases, data is stored across hundreds of separate silos, making data management difficult and inefficient, thus compromising the business insights central to growth.3 In addition, data silos are often reinforced by organisational silos, within which teams utilise disparate tools that rarely interact with one another. The absence of communication between internal teams is reflected in the data, creating a dilemma in meeting consumer demands and recognising critical market trends.
Without a singular data universe, financial services firms struggle to properly leverage analytics tools that rely on compiled data to predict consumer behaviour which can become a huge market disadvantage.
Dealing with cyber threats
Because of the nature of their business, financial services firms face cybersecurity attacks 300 times more frequently than organisations in other industries.4 While the average US business must fend off 4 million cyberattacks per year, a typical US financial services company experiences 1 billion on an annual basis, which can result in much more serious consequences. In 2017, the financial services sector incurred an estimated $18.28 million in costs due to cybercrime, significantly higher than the $11.7 million average cost among other industries.5
Many across this industry dependency on outdated technology makes them vulnerable. Cisco’s 2016 Annual Security Report found that many businesses, including financial firms, were running outdated software in their network infrastructure. The report concluded that although connected and digitised IT and operational technology are vital elements of any enterprise today, “many organisations rely on network infrastructures built of components that are old, outdated, and running vulnerable operating systems, and are not cyber resilient.”6
Absence of security certifications
Financial institutions must ensure that the technology they utilise enables them to operate in compliance with several industry-driven certification requirements concerning data security. Two of the biggest among these are the SOC 2 accreditation standards of the American Institute of Certified Public Accountants7, and the PCI-DSS certification standards8 of the payment card industry.
The SOC 2 standard consists of compliance requirements that verify the secure practices of organisations that store sensitive customer data in the cloud. To achieve this accreditation, firms must complete a rigorous auditing process assessing their policies and procedures in-line with trust principles focused on security, availability, and confidentiality. Similarly, PCI-DSS security standards apply to any organisation that stores, processes, or transmits cardholder data, and certification requires a firm’s systems to meet strict technical, physical, and operational data security controls.
Keeping a handle on costs
In the face of rising expenses, the upsurge of intelligent automation technologies offers an opportunity for firms to increase revenues and customer satisfaction whilelimiting costs. Automating routine tasks, allows financial services firms to maximise efficiency and service levels as they focus energy on enhancing performance.9
Although consultancy firm Capgemini defines “intelligent automation” as a strategic combination of robotic process automation, artificial intelligence, and business process optimisation, these elements can also work individually. By implementing robotic process automation alone, businesses can realisea 10-25% improvement in cost savings. Applying AI-enhanced automation could potentially bring up cost savings to 30-50%. Looking ahead, analysts estimate that AI will save the banking industry more than $1 trillion by 2030.10
However, the potential of intelligent automation remains largely untapped by financial services firms. Capgemini’s research found that 48% of respondent organisations faced issues integrating their automation platforms with legacy software systems and tools. For automation programmes to run successfully, they require access to the full breadth of a firm’s data kept within multiple systems. Legacy systems can pose a serious hindrance to business growth in this area, especially since they are also particularly vulnerable to cybersecurity threats.
Rigid methods impact global competitiveness
Those doing business on a global scale must be able to prepare multiple plans and execute them successfully to adapt to ever-changing circumstances. This agility requires a platform that allows for rapid changes. At the same time, technology is revolutionising the banking experience for clients, making the ability to flexibly wield key technologies and deliver superior customer-facing features a prominent competitive battleground.
However, in a PwC Global CEO Survey, 70% of leaders in financial services reported the speed of change in technology as a concern.11 At the same time, fast-moving changes in technology are increasing the pace of change in non-technological areas as well. Financial institutions must have the agility to quickly and effectively adapt to these transformations.
Intensifying regulatory complexities
Regulators are rapidly adopting a wide range of data gathering and analytic tools in an effort to monitor the industry more effectively and predict potential problems. As financial institutions proceed to automate controls and monitor their processes to comply with new regulations, they will need to make the transparency of their data and control systems a priority.
Financial services firms receive huge amounts of data, but often find it too complex, expensive, and time-consuming to sort through. This has led to the rise of Regulatory Technology (“RegTech”)12 firms, which assist organisations by combining complex information and data from an institution’s previous regulatory failures in an effort to predict potential risk areas that the institution should focus on. RegTech is the management of regulatory processes within the financial industry through technology, and includes providers that utilise cloud computing solutions to help businesses comply with regulations efficiently and at a lower cost.
Using data to enable faster growth
Most of the challenges faced by financial institutions boil down to the shortcomings of legacy systems and difficulties in managing data. Typically, financial institutions work with two kinds of data. The first is enterprise data, which is high-quality structured data — such as customer information, contracts, or financial transactions. The second type is big data, which is high-volume unstructured or semi-structured data such as text files, emails, social media streams, SMS messages, images, or videos, which typically lack governance and security.
To alleviate the data challenges at the root of so many other trouble areas for financial services firms, businesses need to define a data strategy: a plan specifically designed to ensure that data is well-manged and utilised as an asset. However, as per a study by McKinsey, only 30% of banks reported having a data strategy in place.
Identifying tangible use cases
It’s also important for financial institutions to correctly identify use cases where data can provide tangible value. This could mean providing clear insights into what financial products customers are likely to buy, based on what products they use currently and how they use them. It can also mean building applications that enable reaching more of these customers faster, and selling new products and services to them before competitors.
Other use cases financial institutions can address using real-time data include:
Unfortunately, at most firms, the link between enterprise data and big data is missing, making it difficult to operationalise data and drive valuable benefits and actionable insights. Prioritising the use cases, achieving harmony between enterprise and big data, ensuring realistic goals and measurable deliverables, and implementing a well-defined data strategy are essential to successful digital transformation.
As financial services institutions look to modernise their payment methods to help meet the needs of a new wave of customers, they must first overcome the drawbacks associated with their legacy systems. Doing so will bring their operations in line with modern technology and allow them to reap the cost and time savings associated with the adoption of digitisation, allowing them to focus their energy on serving their clients more effectively.