Open, Digital and Future Banking : A Future Ready Banker

Explore why traditional banks shift to digital transformation, through a DBS case study, highlighting faster loan approvals and client-centric services amid rising fintech competition.

DBS case study part 2 traces Strategy 2.0's digital transformation, the core digitalization, customer-centric and invisible banking, and a startup culture built on agile, risk-taking.

Explore why DBS begins its digital transformation to tackle fintech threats, inspired by Alibaba and Alipay, and how regulatory changes and 2008 financial crisis shape the shift to digital banking.

The DBS balanced scorecard blends traditional KPIs with 'making banking joyful' metrics to drive sustainable growth, risk and compliance, customer satisfaction, employee engagement, and digital transformation across regional markets.

DBS advances digitalization through the Gandalf initiative, shifting from project to platform, organizing for success, building agile teams, automating everything, insourcing tech talent, and moving to cloud-centric, modern systems.

DBS shifts to a customer-centric, data-driven approach by mapping customer journeys, digitalizing services, and rewarding digital adoption to cross-sell wisely and embed data culture across the bank.

DBS boosts digital engagement by enhancing retail and institutional platforms, launching DBS Pela for P2P payments, QR code payments, and Digi Bank India and Indonesia, plus API-driven ecosystems with partners.

Learn how DBS transformed into a 22,000-person startup by embracing agile, data-driven, customer-obsessed practices, building a learning organization and delivering end-to-end online experiences.

Explore why digital banking is the need of the hour and how a bank transforms with a digital service provider roadmap to connect banks with people and boost profits.

Show how digital banking increases accuracy, productivity, and usability by integrating front, middle, and back office, automating transactions, enabling 24/7 access, and AI/ML-based personalization with KYC and AML.

Explore how a robust digital core underpins open, digital and future banking, detailing front-end, middleware, back-end layers, API connectivity, and security via machine learning, artificial intelligence, and data analytics.

Explore the four emerging forms of digital banking, including banking as a service (BaaS) and banking as a platform, which connect fintechs and non-banks to banks via APIs.

Explore banking as a platform (BaaP) where banks become core platforms, integrating third-party apps, APIs, and open data to offer digital services, ecosystem partnerships, and mobile platform banking.

Explore cloud-based infrastructure for core banking, leveraging scalable, pay-per-use resources, APIs, and microservices. See how cloud-native architecture enables 24/7, resilient, flexible digital banking with on-prem and off-prem options.

Discover how white label banking enables banks to provide third parties access to proprietary APIs to develop BNPL products under their branding, with streamlined onboarding, loan authorization, and payment reminders.

Discover how a customized CRM enhances ease of banking for customers and employees, tracks client information, appointments, emails, and messages, and manages leads in the sales pipeline.

Explore how a unified banking crm delivers a 360 degree view, real-time notes, and personalized, proactive service to boost customer retention, cross-selling and upselling, and productivity across channels.

Discover how API banking uses application programming interfaces in XML or JSON to enable secure, real-time data exchange between banks and clients and seamless ERP integration.

Explore three broad categories of bank APIs—payments, collection, and financial—and examine real-time fund validation, refunds, cash collection, and status updates, plus MIS and account-related APIs.

Discover how an omnichannel structure delivers real-time, consistent financial services across online and offline channels, enabling seamless onboarding and a unified user experience across devices.

Explore omnichannel modules and mobile banking that run on iOS, Android, and Windows, enabling easy adoption, data unlocking, and personalized services aligned with consumer preferences.

Explore how digital onboarding automates client enrollment, with facial recognition and KYC/AML checks, enabling faster, compliant account opening and secure e-signature.

The third omnichannel module personal finance management gives banks more visibility into customers' finances via mobile and online banking, enabling new value-added services and differentiation of online propositions.

The merchant portal provides an online platform for banks and merchants to manage specials, payments, store data, and customer feedback, with tools tailored for SMB needs.

Explore the fifth omnichannel module of contact center banking, improving first call resolution and agent effectiveness while unifying customer data for a seamless, personalized experience across digital and assisted channels.

Explore how mobile wallets save card information on a device, enable fast self-registration, in-store payments, transfers between bank accounts, and contactless options like QR codes and NFC, with budgeting analytics.

Explore how a chat bot module enables banks and insurers to deliver omnichannel, 24/7 services with quick transactional assistance across web and mobile, powered by NLP and ML.

Explore how the digital risk management model centralizes risk across people, processes, and systems to curb digital fraud with data analytics, machine learning, and AI.

Learn how digital credit monitoring uses early warning signals for proactive, collaborative risk assessment, delivering a consolidated view and continuous data-driven decisions.

Explore how machine learning and artificial intelligence analyze data to predict credit risk and guide loan decisions. Compare random forest with traditional statistical methods in handling non-linear patterns.

Explore three machine learning techniques—artificial neural networks, random forests, and boosting—and learn how neural networks model nonlinear patterns with hidden layers trained by backpropagation.

Explore how random forest combines multiple decision trees to predict defaults, using factors like firm size, ebitda to interest expense ratio, assets, and current liabilities to sales.

Boosting weights each decision tree by accuracy, unlike random forests. The elephant analogy shows how following trees give misclassified observations more weight to improve the final prediction.