"While traditional banks have been convenient one-stop shops, many haven't evolved their products in a way that matches the tech-driven pace of change in other industries." - Mckinsey and Company
The financial industry of Singapore is predicted to grow by 33.5% to reach US 2.70 billion dollars in 2024 and continuing the same trend is predicted to reach US 7.58 billion dollars by 2029. This comes in part of a larger trend of the growing financial sector in the Asia-Pacific which according to The World Bank is ‘growing faster than the rest of the world’
"By 2030 leading banks will become a trusted interface for life, embedded within the needs and lifestyle of consumers." - KPMG
In recent years, the banking and finance industries globally have witnessed significant advancements driven by digitization, innovation, and strategic branding. Financial institutions are increasingly adopting digital technologies such as blockchain, artificial intelligence (AI), and machine learning (ML) to enhance operational efficiency, security, and customer experience. Digital banking platforms and fin-tech startups are revolutionizing traditional banking services, offering seamless and personalized financial solutions. Additionally, open banking initiatives are fostering greater competition and innovation by allowing third-party developers to build new financial products and services.
Source:
The Future of Banking and Financial Services
Future of Digital Banking in 2030
The financial industry in any country is the soul of the nation which impacts the day-to-day life of its people to the global economic standing of the country. As nations, governments, and organizations look ahead to transform their financial industries, especially the banking sector digitally, the problem of ‘DATA’ must first be solved.
Data while abundant is not easily acquired or used. Consumers are now becoming more aware of how, where, and when their data is used, and governments are now actively implementing strict data privacy and protection laws. 137 out of 194 countries have now passed legislative laws to protect personal data, with millions of dollars worth of fines issued globally.
However, data privacy and data protection are not the only bottlenecks that financial organizations have to face. Most financial organizations rely heavily on legacy data anonymization techniques to pseudonymize, suppress, destroy, or mask historical data to protect Personally Identifiable Information (PII) and then use this anonymized data for machine learning, analysis, product development, etc. As discussed in our previous article, legacy data anonymization techniques no longer work for the following reasons:
1. Anonymized data is inversely proportional to data utility i.e. the more we anonymize data the more we lose its utility.
2. Anonymized data is not as safe as it used to be and as technology advances the risk of reidentification of personal identifiable information (PII) has increased.
Synthetic data is artificially generated information that replicates the statistical properties of real-world data, offering a robust solution to privacy and availability constraints inherent in the use of actual datasets. Advanced methodologies, including Generative Adversarial Networks (GANs) and variational autoencoders, are at the forefront of synthetic data generation, leveraging complex machine learning algorithms to create high-fidelity datasets. GANs, for instance, employ a dual-network architecture comprising a generator and a discriminator, engaged in a zero-sum game to produce data points indistinguishable from real data. Variational autoencoders, on the other hand, utilize probabilistic encoders and decoders to map input data to a latent space, ensuring the synthesized data maintains the underlying distribution and correlations of the source data. These sophisticated techniques enable synthetic data to preserve the utility and analytical value of original datasets, which benefits advanced data analysis and model training without compromising on privacy. Furthermore, the synthetic data approach addresses the issue of data scarcity and accessibility, particularly in regulated industries such as healthcare and finance, by providing a viable alternative that meets stringent compliance requirements. In simple words,
Synthetic data enables banks to comply with stringent data privacy regulations such as PDPA, GDPR, and CCPA. By using synthetic datasets, financial institutions can perform data analytics and machine learning without exposing sensitive customer information. For instance, a bank can generate synthetic transaction data to test its fraud detection algorithms without risking customer privacy.
Training AI and machine learning models require vast amounts of data. Synthetic data can augment real datasets, providing additional training material that helps improve model accuracy and robustness. For example, a credit scoring company can use synthetic financial histories to train their algorithms, ensuring they perform well across a broader range of scenarios.
Creating realistic testing environments is crucial for developing and refining financial software applications. Synthetic data can simulate various user behaviors and market conditions, allowing for thorough testing. A fintech startup, for instance, can use synthetic data to simulate market crashes and test the resilience of its trading algorithms.
Synthetic data allows banks to innovate without the limitations posed by data scarcity or privacy concerns. This fosters the development of new financial products and services. For example, a bank might develop a new loan product using synthetic customer data to model different risk scenarios and repayment plans before launching it to the market.
Fraud detection systems require extensive datasets to identify patterns of fraudulent activity accurately. Synthetic data can be generated to include various fraud scenarios, enhancing the training of these systems. A payment processor could use synthetic data to simulate credit card fraud, helping improve its detection algorithms' effectiveness.
Banks can use synthetic data to generate insights and personalize services without compromising customer privacy. For instance, a bank might create synthetic profiles that reflect diverse customer behaviors, allowing it to tailor personalized marketing campaigns effectively.
Regulatory reporting often requires detailed and accurate data. Synthetic data can be used to test reporting processes and ensure compliance without using actual customer information. For example, a financial institution can generate synthetic transaction data to validate its anti-money laundering (AML) reporting systems.
Synthetic data can be employed to test and improve cybersecurity measures by simulating various attack scenarios. A bank's IT department could use synthetic datasets to conduct penetration testing and evaluate its security protocols' effectiveness.
Synthetic data can help banks perform comprehensive risk management and stress testing by simulating various economic conditions and their impact on financial portfolios. A bank could generate synthetic economic scenarios to test its capital adequacy and resilience under different market conditions.
One of the key challenges in financial modeling is the lack of data for rare but critical scenarios, such as economic crises or financial market crashes. Synthetic data can be generated to include these rare events, providing valuable training material for predictive models. For instance, an investment firm could use synthetic data to simulate rare market downturns, enhancing its portfolio management strategies and risk assessment frameworks.
Small businesses often face challenges in obtaining credit due to insufficient credit history or limited financial data. Traditional credit risk models may not accurately assess the risk associated with these businesses, leading to higher rejection rates or unfavorable loan terms. Synthetic data can play a crucial role in addressing this issue.
Credit risk models trained with synthetic data can more accurately predict the creditworthiness of small businesses, leading to better credit decisions and reduced default rates.
Small businesses with limited credit history or financial data can benefit from fairer assessments, increasing their chances of obtaining loans and favorable terms.
Using synthetic data helps financial institutions comply with data privacy regulations while still leveraging valuable insights for credit risk modeling.
Synthetic data is a powerful tool that is driving digital transformation in the banking and finance sectors. By enhancing data privacy, accelerating AI and ML training, enabling robust testing environments, and augmenting data for rare scenarios, synthetic data is helping financial institutions innovate and improve their services. As the technology continues to evolve, its impact on the industry is set to grow, making it an indispensable asset for future-proofing banking and finance operations.
