Home > Finance

Financial Services

Cloud Capital, Data Dividends, AI Assets

 Navigating the intricate financial ecosystem Billigence’s expertise extends from traditional financial institutions to emerging neo-institutions. We empower these organisations to address a wide range of challenges, from compliance to customer experience. As fintechs and other new entrants redefine the competitive landscape, we assist established firms and newcomers alike in optimising their operations and staying competitive. 

Common Challenges

  • Tick
    Data Governance and Compliance: Financial institutions face the challenge of adhering to constantly evolving regulations while maintaining data quality and security. Failure to comply can result in substantial fines, legal consequences and reputational damage.
  • Data Prep and Modelling
    Data Integration and Silos: Merging disparate and sometimes outdated data systems into a unified, actionable view remains a significant obstacle. Poor data integration can lead to inaccurate analytics, impacting business strategy and ultimately affecting the bottom line.
  • Migration
    Data Sharing: Securely distributing data internally across business units and externally with clients is essential. Inadequate security protocols can result in data breaches, eroding client trust and incurring legal penalties. This directly impacts the institution's reputation and bottom line.

Use Cases

Data Governance

Implementing a robust data governance framework is essential for ensuring data meets industry-specific compliance and regulatory standards. Financial institutions are employing powerful governance activities like data lineage, data dictionaries and role-based access controls to facilitate collaboration and instil trust in their data.

Data Retention Policy

To minimise the risk of data breaches, financial institutions are setting strict data retention policies that dictate the types and duration of data storage. These policies are designed to keep only what’s necessary for legal requirements, operational efficiency and marketing strategies, while safely discarding or anonymising extraneous data. 

Next Best Action

Cutting-edge AI technologies are now enabling smarter choices by advising on the next best action for individual cases. Businesses are utilising tools like Xction.ai to obtain immediate recommendations based on customer habits, behaviours and their relationship with the products or services offered. 

Risk Assessment and Fraud Detection

Accurate risk modelling is crucial for identifying emerging types of fraud and sudden shifts in consumer behaviour. This is why an increasing number of businesses are transitioning from traditional methods to real-time machine learning solutions, which offer enhanced predictive accuracy and adaptability. 

Bond, Equity & Credit Market Analysis

Data analytics and machine learning are crucial for real-time monitoring of market trends, price movements, and associated risks in bonds, equities and credit markets. These tools empower traders and portfolio managers to make data-driven decisions rapidly, enhancing market agility and risk mitigation strategies. 

Regulatory Reporting

Advanced Business Intelligence tools and data automation solutions are transforming the regulatory reporting landscape. By automating the collection, validation and submission of requisite data, institutions not only ensure compliance but also significantly cut down on human errors, time and resource expenditures. 

Industry Trends

Migrating to the Cloud

There’s a surge in transitioning from on-prem solutions to cloud-native or cloud-ready platforms, particularly as modern analytics tools are being designed with the cloud in mind. The cloud increases data capabilities and collaboration while mitigating concerns around data security and location. For example, Australia’s data sovereignty requirements are met with local data centres, ensuring data stays within national borders and is accessible only to Australians. 

Embracing AI & ML

AI and ML are ubiquitous, embraced by businesses across industries and sizes, including the finance sector. In finance, these technologies are notably enhancing efficiency and reliability in areas like fraud detection and product innovation. As regulatory compliance remains a critical concern, new AI/ML platforms are generally designed to be interpretable, transparent and highly secure, ensuring that innovation doesn’t compromise governance standards. 

Determining Liquidity

Advancements in analytics are revolutionising the way the finance sector is optimising cash reserves. Leveraging sophisticated analytics, firms can now maintain lower cash balances without compromising operations, freeing up capital for strategic investments. As regulatory oversight intensifies, these approaches are becoming both compliant and scalable, ensuring that liquidity management is agile and aligned with governance standards. 

Measuring ESG Impact

Environmental, Social and Governance frameworks are accelerating, fuelled by sophisticated data analytics that allow for the monitoring and improvement of sustainability and inclusivity with unprecedented granularity. Previously unmeasured factors like carbon emissions and workplace diversity are now quantifiable, offering actionable insights for businesses to develop targeted improvement strategies.  

Single View of Customer

Integrating data from various touchpoints—such as transaction history and customer service interactions—to create a unified, 360-degree view of each customer has become the new norm. This perspective improves personalised service offerings and targeted marketing and elevates overall customer satisfaction. It facilitates quick identification of potential issues, enabling proactive engagement to foster customer loyalty.