The changing digital landscape and the multitude of cyber threats are forcing modern financial institutions to adapt or risk falling behind the curve. Financial institutions are now under pressure to strengthen their security posture, while balancing the need to embrace new technologies and the constant battle to ensure compliance with the ever-growing regulatory obligations.
The exponential growth of data further exacerbated the myriad challenges that financial institutions are facing, hindering their ability to understand and utilise their data estate for effective decision making. If robust data management strategies are not put in place, financial organisations could be hit with long-term data fragmentation and find themselves losing ground to their competitors.
The burden of data fragmentation
Today, data fragmentation continues to plague many financial institutions, with data stored in silos, sprawling across different systems, resulting in IT complexity and lack of visibility. For instance, there will be inconsistencies in valuations and risk calculations when the data sets are feeding into different models or incorporated at different frequencies. These multiple data sources can create disconnects across the front, middle and back offices, resulting in higher operational risks, costs and inefficiencies.
The sheer volume of data generated across numerous platforms and systems prove to be a significant obstacle for many financial institutions, as they seek to integrate and manage these complex data sets to support critical decision-making, business operations or inform investment strategies.
To address the issues of data fragmentation, it is paramount for financial institutions to regain control of their data to establish and maintain data lineage. As a first step, financial institutions will benefit from deploying proper classification tools and policies to understand what data they have, where it is located, who is using it, the number of copies that exist, if it is valuable or not, and more. Unless a financial institution understands what data it has and where, it is impossible to manage, let alone conduct any analysis to draw insights for future trends and investment purposes.
On top of data fragmentation, financial institutions also need to keep up with an increasingly complex and demanding regulatory environment governing the creation, storage and utilisation of data. To address this, financial institutions can consider bolstering their capabilities to safeguard data and enhance visibility by building a map of where data is being stored for timely retrieval, setting access control, deploying and enforcing retention policies that automatically expire data over time, and monitoring for possible breach activities for swift action. This is also where the adoption of new technologies, such as artificial intelligence (AI), machine learning (ML) and cognitive automation come in useful. These tools not only alleviate tedious tasks but also swiftly identify patterns, trends and anomalies that might otherwise be undetected. By automating their compliance and governance processes, financial organisations can combat data fragmentation, and flag potential financial crime activities in real-time, to ensure timely adherence to regulatory requirements.
Generative AI in the financial sector
As AI gains momentum as a strategic imperative, its impact is growing across multiple industries, including the financial sector. In Singapore, DBS is leveraging generative AI to augment the way employees work by handling routine tasks, allowing them to focus on higher-value activities, such as cultivating deeper customer relationships. It has also launched DBS-GPT, an employee-facing version of ChatGPT, used by over 5,000 employees in Singapore, to help them with content generation and writing tasks in a secure environment. Generative AI is also being used to extract information from documents and populate into templates for new trade loans after analysis.
Similarly, Morgan Stanley has leveraged Open AI’s Gen AI capabilities to deploy an employee-facing chatbot for their wealth management division, enabling their teams to ask questions and contemplate large amount of data, with answers, along with links to the source documents, delivered in an easily digestible format.
Generative AI also holds great promise in the early detection of financial crime by reducing manual intervention, minimise errors and enhance the accuracy of detection mechanisms. For example, Westpac New Zealand integrate generative AI into their fraud detection systems to combat sophisticated fraud schemes. By utilising data analytics to monitor and predict illicit behaviours from diverse data sets, generative AI models can proactively flag deviations, aiding in anomaly detection crucial for fraud prevention within the financial sector. Additionally, generative AI can simulate fraud scenarios and generate synthetic data, enhancing training sets for fraud detection algorithms and augmenting their accuracy.
The adoption of generative AI, along with data privacy mandates, will continue to accelerate. Data security will remain a top priority for financial institutions to ensure trust between customers and banks is not compromised. It is imperative for financial institutions to put guardrails with effective AI guidelines and policies, along with appropriate AI tools in place, to mitigate data security and privacy risks. For a start, this includes conducting comprehensive risk assessment to identify potential vulnerabilities, evaluating data storage practices, access controls and data sharing protocols to ensure compliance with relevant regulations. For example, banks who are making their initial foray into generative AI can experiment in very contained environments to ensure sensitive information is not make available to the public.
It is also necessary to educate employees on the safe and secure generative AI usage, with a particular emphasis on data protection, security and compliance requirements. That said, data policies would only be truly effective if financial institutions are implementing good data governance. This includes establishing frameworks for ongoing monitoring, auditing, and enforcement of data policies to detect and address compliance breaches proactively.
Partnering for success
Safe and accurate data are the foundation for financial institutions. Data governance should be a priority, not afterthought. Enriching and authenticating data between parties can enable institutions to use their data more intelligently, make better decisions and address real customer needs.
With AI advancing at a rate faster than most organisations can keep up with, financial institutions can consider partnering with trusted providers like Veritas to mitigate the risks associated with generative AI and other emerging technologies. By protecting the organisation’s critical data and IT infrastructure, ensuring data is backed up securely and providing advanced threat detection capabilities with comprehensive visibility across the IT landscape, data management providers play a pivotal role in helping financial institutions to stay compliant with regulatory requirements while minimising costs and cybersecurity threats.
Done right, financial institutions can protect themselves from potential harm and unlock the full potential of AI technologies to drive growth and innovation.
This opinion piece is written by Mr Andy Ng , Vice President and Managing Director for Asia South and Pacific Region, Veritas Technologies.
