How Can Traditional Financial Services Respond to FinTech Disruptors?

How Can Traditional Financial Services Respond to FinTech Disruptors?

Traditional financial services companies (FSIs) such as credit unions, banks, credit-card issuers, insurance providers, and stock brokerages face several challenges ranging from ageing IT Infrastructure and legacy systems to inconsistent customer experience, increasing regulatory risk, and fierce competition from FinTech disruptors such as Fundbox, SoFi, Plaid, Blend, and the like. Traditional financial players need to fully harness the power of data to improve customer experience, IT & business operations efficiency, and regulatory compliance.

1. Embrace the Cloud

Cloud infrastructure delivers both economic value and business agility. It helps companies scale on-demand usage and quickly connect with value-added business partners to improve customer experience and functionality. The rapid adoption of public cloud by some of the largest SaaS applications in the world such as salesforce.com, Workday, Netsuite and others, has proven that companies across all industries believe their data is safe in the cloud. Strikingly, many regulators are adopting the public cloud for their applications, indicating their comfort with the ability of cloud vendors to meet regulations. A cloud-based Self-Learning Data Platform provides fine-grained, attribute-level, visibility of who searched for, who looked at, and who modified what data. In addition, it undergoes regular third-party security penetration testing to detect and close any security holes. Read this white paper Cloud Data Management and Applications - Why It's Safe and Secure to learn more.

2. Leverage Agile Data Architecture

A Self-Learning Data Platform offers a technology architecture that is better aligned to the lines of business and capable of responding to FSIs’ future business models. For example, it can participate in customer journeys—such as shopping, travel, and vacation—as opposed to product-level processes, account opening, and onboarding. The multi-domain meta model allows for agile changes to support business needs. In addition, one doesn’t have to spend several months to upgrade the system. With no fixed schema and a NoSQL storage layer, data model is quickly configured and changes can be made in minutes to adapt to evolving business needs.

3. Drive Operational Excellence

A Self-Learning Data Platform helps financial enterprises continuously organize all data about their customers, products, compliance, and regulations, as well as business transactions, into a reliable and secure data foundation. Once data is organized, enterprises provision the consistent and clean data to all operational and analytical systems to improve operational efficiency thus reducing operational cost and cost of compliance. For example, it can help financial companies smoothen operations for customer onboarding, transaction monitoring, and regulatory compliance reporting. Likewise, it helps facilitate faster and better risk rating, credit, or coverage decisions through a better understanding of the applicant.

4. Deliver Superior Customer Experience

Data silos across departments, products, and geographies lead to inconsistent customer information across different business units within a financial organization. It leads to disjointed customer experience, raised costs of serving customers, and reduced share of wallet. A Self-Learning Data Platform provides detailed views of clients with complete profile, interaction and transactional data converged from multiple internal and external sources. It allows to deliver consistent information across internal teams, across channels (mobile applications and e-commerce platforms and so on) and throughout the client journey to deliver great customer experiences and frictionless banking. In addition, financial institutions can understand the total worth of the household to make relevant offers rather than making decisions based on individual disconnected accounts owned by different family members. The Self-Learning Graph, for example, can help group consumers into family units based on an individual consumer’s social connections, locations, and purchased products.

5. Meet All Regulatory and Compliance Needs

Maintaining regulatory compliance is a top and ongoing concern for financial services companies. Inability to stop data breach and meet ever-changing regulatory requirements result into downgrades in regulatory ratings and sometimes punitive action by the regulatory agency. Also, it inhibits FSIs’ ability to leverage new business models that necessitate sharing customer data outside of the FSI relationship. A Self-Learning Data Platform provides comprehensive auditing and tracking features to guarantee compliance by managing a historical trail for any data merged or updated. It allows FSIs to respond to a growing number of legislative and regulatory initiatives including Comprehensive Capital Analysis and Review (CCAR), Foreign Account Tax Compliance Act (FATCA), Know Your Customer (KYC), and new regulations such as GDPR by providing accurate and timely compliance reporting.

6. Expedite Post M&A Requirements

Mergers and Acquisitions (M&A) continue as financial institutions struggle with the costs of regulation and strive for growth in a more competitive marketplace. A Self-Learning Data Platform provides built-in mergers and acquisitions capabilities for powerful pre-merger analysis and accelerated post-merger integration. It helps expedite post M&A requirements by onboarding, rationalizing, and delivering customer, product, employee, and other critical business data to support consolidated financial reporting, risk management, cross-sell marketing campaigns, and other post M&A business needs.

Adoption of a cloud-based Self-Learning Data Platform can enable traditional financial services companies to deliver faster, affordable, and more personalized products and services in real time that meet the requirements of regulators while also better serving the needs of their customers.