Five key trends in financial services

1. Mobile-first super apps

While mobile penetration varies from country to country, it is estimated that around two thirds of the world’s population own a smartphone. Consumers of financial services are predominantly mobile-first and are looking for convenience, reliability, security, and speed – and that means the ability to access their financial life through a single ‘super app’.

Super apps provide users with easy and convenient access to financial services, at any time and from anywhere. But there are also clear benefits for financial services firms. A super app allows firms to access more customer data which can be used to personalize services for each user. Consolidating all services into a single super app creates economies of scale in terms of development, maintenance, and marketing. But there are risks too, not least in terms of data security.

2. Open and commodity banking

The rise of open and commodity banking is a double-edged sword for established financial services firms; it is encouraging competitive fintech firms to flood into the market, but it also creates the potential for new revenue streams. These include:

• Subscription-based services such as personalized financial advice or premium account management services
• Cross-selling to existing customers
• Monetization of customer data (provided customer privacy is maintained and data is collected and used ethically).

Open banking is encouraging the formation of mutually beneficial partnerships between fintechs and established players, including ‘white label’ arrangements, where fintechs license their technology and services for other businesses to use under their own brand in exchange for a share of revenue.

3. Modularity

Success in a highly competitive market increasingly depends on an institution’s agility – their ability to respond rapidly to new opportunities and challenges and develop and integrate new services quickly. This has been particularly difficult for established institutions; legacy systems have proven a serious handicap as they try to compete with nimble tech-driven entrants to the market.

In response many firms are adopting modularity, a flexible approach to developing fintech products and services that allows them to adapt to changing market conditions, emerging trends, and customer needs. By breaking down services into smaller, modular components, new features and services can be added with less disruption to the system.

A modular approach allows firms to easily scale up and down and helps to manage development and innovation costs – errors can be detected early in development, helping to avoid costly mistakes, and components can often be used in new services, further reducing development time. The time-to-market is also reduced, since each component is developed and tested separately (and often concurrently).

4. Personalization of the customer experience

Artificial intelligence (AI) is already revolutionizing the way customer support is provided – chatbots can offer a reliable, 24/7 service and resolve issues quickly and efficiently. But AI also provides a route for greater personalization of the customer experience. AI and machine learning algorithms analyze both a customer’s financial data and their behavior, which allows institutions to offer personalized services and recommendations that help customers make better financial decisions.

5. Superior fraud detection and cyber resilience

Fraud and cyber security are a constant challenge for all financial services firms. A recent survey by PwC found escalating levels of fraud and cyber-attacks in the financial services sector; 44% of firms questioned said they had experienced customer fraud in the past two years, and 38% had experienced cyber-crime.

Firms must balance stringent checks and compliance requirements with customers’ demands for a rapid, seamless experience. While criminals are becoming increasingly inventive, innovative technology is a powerful tool that is helping institutions manage fraud and security risk without impacting the customer journey. Machine learning and AI algorithms allow for the near-instantaneous analysis of vast amounts of transactional data, including a customer’s financial history and behavior, to rapidly detect anomalies and identify potential security threats. Biometric authentication methods such as fingerprint scanning, facial recognition, and voice recognition are adding an extra layer of security.