|
Getting your Trinity Audio player ready...
|
This article is the first of a two-part series. You can read Part 2 here.
Data is more critical than ever as financial institutions in the Philippines navigate growing complexity and competition. The increasing adoption of artificial intelligence (AI) in financial analytics enables organisations to foster innovation, enhance operational efficiency, and make more informed strategic decisions.
However, the effectiveness of AI-driven financial systems heavily depends on the quality of the data utilised. Data mismatches, caused by issues like inconsistent formatting or entry errors, lead to inaccurate insights and analyses. These discrepancies weaken the accuracy of financial information and erode trust in AI-driven decisions.
Moreover, when using traditional tools, 80% of the effort is dedicated to manual data preparation, leaving only 20% for actual analysis. Introducing visualisation tools can add complexity, potentially leading to errors that compromise data integrity. This fragmented approach not only diminishes trust in financial analysis but also limits AI’s overall effectiveness.
While high-quality, structured data is essential for unlocking AI’s potential in the finance sector, the integration of additional tools must be carefully managed to avoid complicating workflows and undermining accuracy.
For Philippine financial institutions, investing in robust data frameworks is essential. Clean, reliable data enables AI to deliver accurate forecasts, spot trends, and support informed decisions. Without high-quality data, even advanced AI can produce misleading results, undermining strategic goals.
Maintaining high data quality is essential but challenging. Many Philippine organisations face issues like fragmented sources, inconsistent formats, and outdated technology, which can result in unreliable AI outcomes. Overcoming these obstacles requires a robust strategy that incorporates standardised processes, modern management tools and regular quality checks to ensure accuracy and consistency.
Finance professionals can effectively handle complex risk and compliance challenges by integrating diverse data sources and aligning governance models with organisational objectives. This strategy enhances transparency, reduces risks, fines, and fraud, and promotes a culture of strong regulatory adherence. A unified data approach and solid governance framework strengthen organisational resilience and safeguard financial integrity in an evolving market.
As the financial sector in the Philippines continues to evolve, AI is becoming increasingly essential in financial analytics. It is revolutionising data optimisation in the Financial Services Industry (FSI) by enhancing the processing of large datasets and revealing hidden trends. AI strengthens risk management, streamlines workflows, and speeds up tasks such as data purification and visualisation. While advanced analytics offer a competitive advantage and support informed decision-making, robust data governance and transparency are necessary to fully capitalise on these benefits.
By prioritising data quality, organisations can maximise AI’s potential, driving better financial outcomes and meeting strategic goals. A strong focus on data excellence not only improves operational efficiency but also positions businesses for sustained success in a competitive market.
The OpenGov Breakfast Insight on 25 September 2024, at Shangri-La the Fort Manila, explored the transformative impact of advanced financial management solutions on the Financial Services Industry (FSI) in the Philippines.
The event brought together industry leaders, IT specialists, and data experts to discuss the latest advancements in data quality for AI-driven financial analytics. Attendees explored innovative trends, transformative technologies, and best practices shaping the future of financial data management in the Philippines.
Opening Remarks
Mohit Sagar, CEO and Editor-in-Chief, OpenGov Asia underscores the growing importance of data analytics in the banking and financial sectors. The integration of advanced analytical tools significantly enhances decision-making, risk management, customer engagement, and regulatory compliance.
In today’s fast-paced market, accurate and timely financial information is essential for survival. Financial analytics play a vital role in this context, enabling organisations to process vast amounts of data and transform raw information into actionable insights. This not only boosts operational efficiency but also guides strategic planning.
By leveraging authentic, granular customer data, analytics can unlock the potential for personalised services tailored to individual needs and preferences. This tailored approach not only enhances customer satisfaction but also fosters deeper relationships, allowing businesses to anticipate client requirements and adjust their offerings accordingly. Ultimately, harnessing detailed customer insights leads to more effective decision-making and a competitive edge.
Maintaining high-quality data is crucial for operational integrity and regulatory compliance within financial institutions. Poor data quality can result in inaccurate risk assessments, financial losses, and diminished client trust.
“We often blame the data, but who’s really at fault? Data biases usually stem from past organisational policies and methods,” Mohit points out. “The problem often is not with the data itself, but with how it has been managed over time.”
Mohit encouraged organisations to take responsibility for their data governance practices, calling for a shift in mindset. By acknowledging and addressing the historical factors that contribute to data biases, organisations can foster a more accurate and fair use of data, ultimately leading to better decision-making and outcomes.
AI’s transformative role in financial analytics is undeniable, improving predictive accuracy, risk management, and decision-making by extracting insights from unstructured data. As the business landscape evolves, leaders are increasingly empowered to leverage advanced technologies for more informed, data-driven strategies.
“CEOs worldwide now hold a significant advantage, recognising the immense value of data and having access to vast amounts of it,” Mohit observed. “They are no longer just passive recipients of information but are actively leveraging data as a strategic asset, using AI technologies to propel their businesses forward.”
The Philippines stands at the brink of a promising future in AI-driven financial analytics, with its financial institutions well-positioned to harness AI to enhance client experiences, streamline operations, and manage risks more effectively.
“But how do we truly unlock insights from the data we already have?” Mohit asks, encapsulating the session’s core theme and urging delegates to rethink their data strategies. “By prioritising data quality, investing in AI, and cultivating a culture of continuous innovation, financial institutions can not only reach their full potential but also drive meaningful growth in the Philippine financial sector.”
Welcome Address
JJ Tan, Regional Sales Director for ASEAN, Qlik, underscored the critical role of data in the banking and finance sectors, highlighting Qlik’s exemplary data management capabilities as a key driver for enhancing financial solutions.
As the financial sector undergoes rapid digital transformation and increasingly adopts AI technologies, the need for accurate, reliable, and high-quality data has become a strategic priority.
Since data is the backbone of AI, strong data management practices are essential for ensuring the optimal performance of AI models. Without well-managed, high-quality data, AI-driven analytics cannot produce accurate or dependable insights.
“There are widespread challenges organisations face with data management, especially when it comes to scaling AI,” JJ acknowledges. “In fact, over 72% of leading organisations identify data management as a major obstacle to adopting AI technologies.”
Inadequate data input can yield ineffective AI results, potentially resulting in bias issues that banks must navigate carefully due to regulatory scrutiny. JJ points out that discussions around data quality have grown more complex with the advent of digital banking, despite having a long-standing history.
While data governance challenges, such as GDPR compliance and data breaches, have been around for some time, the rise of digital banking has heightened these concerns. Many banking clients continue to struggle to ensure data integrity and governance of their organisation’s official shared master data assets.
“Although the concept has existed for decades, it is still highly relevant as banks work to deepen their understanding of customer journeys and refine their sales strategies,” JJ highlights the importance of master data management. “An effective strategy is crucial for financial institutions looking to enhance client engagement and drive revenue growth.”
JJ expressed caution regarding the competitive landscape, highlighting that banks are not only up against other financial institutions but also tech companies with superior data targeting capabilities.
“The competition is intense, and those who can harness data effectively will gain a considerable edge,” he stated, “Banks must innovate and adapt to remain relevant in a rapidly evolving industry.”
As the financial sector embraces digital transformation, high-quality data will be crucial for long-term success, allowing institutions to maximise AI’s benefits and build more sustainable, resilient, and forward-thinking organisations.
JJ concluded by highlighting Qlik’s dedication to using Gen-AI responsibly to revolutionise data analytics and business intelligence. By focusing on data quality, privacy, and security, Qlik is dedicated to fostering an ethical AI culture that empowers organisations to make informed decisions and drive responsible innovation in a data-driven world.
He reassured attendees that Qlik is prepared to support organisations through its steadfast commitment to ethical AI practices and data stewardship, ensuring reliable advancement and success in the changing data landscape.
Technology Insight
Sunit Kapoor, Business Strategy and Value Engineering Advisor, Qlik, is firmly convinced that data serves as the foundation for any industry, regardless of size or sector. When combined with the right strategies and tools, data can become a powerful driver of growth, innovation, and competitive advantage.
Sunit highlights that the effectiveness of AI hinges on the quality of the data it receives, underscoring it with the well-known adage, “garbage in, garbage out”. While AI has the potential to transform businesses, its success primarily relies on the integrity and quality of the data it processes.
For AI to generate meaningful insights, the data must be of high quality, accuracy, and relevance. Insufficient or poor-quality data can result in inaccurate forecasts, flawed algorithms, and unpredictable outcomes. Regardless of how advanced the AI model may be, it will only deliver value if built on a strong foundation of superior data.
To maintain data accuracy and consistency across all levels of the organisation, effective data governance, rigorous data quality standards, and continuous improvement efforts are essential, as these practices foster a culture of accountability and trust in the data being utilised.
He outlined the “Five Key Needs for Accelerated AI Adoption”, which serve as a strategic roadmap for organisations aiming to realise the full potential of AI. These foundational requirements help navigate the complexities of AI integration.
Transformation: Successful AI adoption necessitates a shift in data management and technology infrastructure that prioritises data acquisition, integration, and transformation. By embedding artificial intelligence into workflows, organisations can foster faster innovation and greater efficiency.
Trust: Users must have confidence in AI systems, which requires strong data quality, integrity, and transparency in decision-making processes
Sunit stresses the importance of cultivating trust in data and presents the idea of a “trust score” to continuously evaluate data quality. This is a comprehensive cycle for managing data quality, which encompasses onboarding new data, identifying and resolving issues, standardising formats, and conducting regular quality assessments.
Access: To accelerate AI adoption, it is crucial to dismantle data silos and integrate systems, ensuring seamless data access. Having access to both internal and external data enables AI to deliver comprehensive insights, leading to more informed and accurate decisions.
Analysing, predicting and responding: AI excels at processing vast datasets, identifying trends, and addressing complex business challenges. Investing in real-time AI tools allows organisations to make timely predictions and adapt quickly to changing circumstances.
Action: This highlights the importance of implementing AI-driven insights. To fully capitalise on their transformation initiatives, organisations must develop frameworks that enable the swift application of AI recommendations, ensuring they can reap the benefits of their investments.
Sunit encouraged delegates to reach out to him and his team at Qlik to explore how they could them in their journey towards data-driven and AI-powered analytics transformation.
“Whether you’re looking to enhance data quality, integrate advanced tools, or develop effective governance frameworks, Qlik is here to support you in unlocking the full potential of your data,” he assured them.
This article is the first of a two-part series. You can read Part 2 here.
