Introduction  

In today’s banking landscape, the speed of credit decisions—often referred to as “time to yes”—has become a critical competitive factor. Banks are increasingly evaluated based on how much time elapses from when a client first approaches a bank for a loan and when they receive a formal offer or approval. Thus, to win deals, banks must strike a balance between meeting client expectations for speed while also maintaining high-quality service with robust risk compliance. Let’s take a look at how this has unfolded.

Why “time to yes” matters more than ever

Historically, banks have relied on lengthy, paper-based approval processes with multiple layers of review. While this helped mitigate risk, it often frustrated clients, particularly those businesses needing quick financing decisions.

Despite some changes, several interconnected challenges remain, impacting different lending segments:

• Corporate: Large-ticket, corporate lending approvals are often subject to governance frameworks that lack adaptability. Uniform approval processes are applied to all deals rather than differentiating between routine transactions and complex, high-risk exposures. 
• Retail: Many banks have digitized parts of their consumer/mortgage lending processes but still struggle with legacy system constraints and inconsistent data utilization, fragmenting customer journeys and credit decisions.
• Small and medium enterprise (SME): Most banks adopt a scaled-down version of corporate credit processes but often lack the appropriate data models to assess SME risk efficiently. Consequently, time saved between corporate and SME lending requests – if any – is far below potential, making approvals unnecessarily slow and resource-intensive.

However, several key trends are driving a fundamental shift towards faster, more efficient loan approvals:

• Tougher competition in lending: Digital-first lenders and fintechs have significantly shortened credit approval times, creating pressure on traditional banks to keep up.
• A difficult macroeconomic environment: Geopolitical and economic uncertainty mean clients are more cautious and expect quick, seamless support from their bank.
• Evolving customer expectations: In an era where near-instant service is expected—from digital payments to e-commerce transactions—paper-based documentation request and waiting days or weeks for a credit decision feels outdated. 

For banks, the challenge is clear: Speeding up time to yes is no longer optional—it’s essential for remaining competitive, improving client experience, and driving growth. A shorter time to yes leads to:

• Better customer experience: Faster decisions free up commercial teams to focus on deeper client engagement, enhancing relationships, acquisition, and pricing power.
• Improved quality of “yes”: More effective data use supports more efficient risk assessments, allowing teams to prioritize high-impact, complex deals that could potentially have more negative consequences.
• Operational cost savings: Eliminating redundant processes and automating low-value activities enhances efficiency. 

A three-pronged approach can help banks achieve these results:

1. Optimizing processes to eliminate inefficiencies
2. Streamlining governance to enable faster decision-making
3. Leveraging technology and data more effectively 

Optimize processes for speed and efficiency

One of the biggest obstacles to faster loan approvals is outdated and fragmented credit processes. Many banks still operate with manual workflows, excessive documentation requirements, and redundant approvals, leading to unnecessary delays. Loan origination requires seamless collaboration across business, operations, risk, policy, credit decisioning, data, and IT functions. However, in many banks, these departments operate in silos, creating inefficiencies.

Enhanced loan approval processes

To accelerate credit decision-making, banks should implement the following solutions:

• Integrated digital workflows: Automate data-sharing across departments to eliminate redundant manual tasks and improve collaboration.
• Standardized approval criteria: Establish consistent policies across business lines to enhance efficiency and reduce inconsistencies.
• Smart data utilization: Leverage AI-driven insights and historical data to enable faster and more accurate risk assessments 

Deloitte case study (ongoing):   A Benelux bank reduced mortgage approval times from 15-20 days to 3-5 days by digitizing credit review, collateral valuation, and underwriting processes.

Self-service to reduce time to yes

One of the most effective ways to shorten the approval timeline is by implementing self-service digital platforms where clients can actively participate in the loan process.

• Client-driven document uploads: Instead of waiting for Relationship Managers (RMs) to gather and transmit financial statements to the necessary units, businesses and individuals can upload documents directly onto a bank’s portal.
• Pre-populated forms and real-time eligibility checks: Online platforms can auto-fill applications using data already available within the bank, reducing manual errors and back-and-forth communication.
• Automated tracking and status updates: Clients can check the status of their application at any time, eliminating unnecessary delays caused by manual follow-ups. 

By shifting part of the process to the client side, banks can free up RM resources, improve efficiency, and significantly reduce time to yes.

Streamline governance for faster credit approvals

Even with optimized processes and advanced technology, many banks still struggle with rigid governance structures that slow down approvals. Due to a lack of confidence in data and automated decision-making, the previaling culture often results in non-standardized decisions based on personal judgment. A modern governance strategy should balance risk management with efficiency, ensuring that policies enable, rather than hinder, decision-making.

To improve time to yes, banks should move towards a risk-based delegation framework that includes the folllowing:

• Automated approval thresholds: Approve low-risk loans instantly, reserving manual reviews for complex cases.
• Tiered decision-making models: Define clear approval hierarchies based on loan size, borrower profile, and risk exposure.
• Empowered frontline staff: Provide relationship managers with more decision-making authority for pre-qualified clients.
• Data-driven governance: Utilize real-time analytics and predictive modeling to support decision-making and reduce reliance on discretionary approvals.
• Adaptive credit policies: Implement flexible policies that adjust based on macroeconomic conditions and risk appetite, allowing for dynamic scaling of approval thresholds. 

With these governance changes, banks can reduce unnecessary escalations, ensuring that only high-risk loans require full credit committee review. This also enhances accountability and transparency, fostering a governance structure that supports both regulatory compliance and business agility.

Leverage technology and data for smarter decisions

Technology is at the core of transforming credit decision-making. Many banks, however, struggle with legacy systems that limit qualitative data access, collaboration, and automation capabilities.

Redesigning the end-to-end process does not necessarily imply a greenfield IT approach; a new front-end can be built on top of the existing core banking IT. Some players have opted for cloud-native stacks, merging customer relationship management (CRM), loan origination, and collateral management capabilities. Banks are also partnering with fintech players to revitalize offerings and digitize processes across the lending value chain.

Key technology enablers for faster credit approvals

To streamline and accelerate credit decision-making, banks should leverage the following technology capabilities:

• Expose data insights and guidance to advisors early in the process.
• Integrate internal and external data to improve collateral valuation.
• Automate underwriting.
• Digitize credit proposal papers and automate the review process. 

The impact of effecitve data use

Many banks already collect valuable customer data, but fail to leverage it efficiently. By integrating internal datasets into real-time credit decisioning, banks can do the following:

• Make data-driven lending decisions with greater accuracy.
• Enhance risk assessment.
• Speed up the approval process. 

For example, a retail customer who has held a bank account for years with a stable income pattern could be pre-approved for a personal loan instantly, based on their transaction history, without needing additional documentation.

The next moves banks should make

For banks looking to accelerate their credit approval process, the transformation does not have to be overwhelming. A structured, step-by-step approach that prioritizes speed and efficiency, streamlines governance, and makes the most of their inteneral data, can help identify and mobilize quick wins while setting a foundation for long-term efficiency.

Actionable steps for banks

• Conduct an “As-Is Assessment”: Identify bottlenecks in current lending processes and pinpoint low-hanging fruit that can immediately enhance efficiency.
• Leverage internal data: Review how existing customer data is being used (or overlooked) in the credit decision process and integrate real-time analytics where possible.
• Implement digital self-service options: Enable clients to upload documents, track applications, and receive pre-approvals online, reducing manual workloads for RMs.
• Define a digital transformation vision:
  • Identify the highest-value use cases by customer segment/product.
  • Adopt a holistic approach to avoid inefficiencies caused by isolated quick wins.
  • Focus on data capture, process digitalization, collaboration, and efficient governance.
  • Automate checks, ratio calculations, and credit memo generation.
  • Prioritize mortgage or SME lending as a starting point for transformation. 

By taking these steps, banks can start seeing measurable improvements in their credit approval process within months—not years.

The Future of loan origination with Generative AI

The next wave of transformation is already underway with the adoption of Generative AI (GenAI)—a powerful leap beyond automation and predictive analytics without compromising on compliance, transparency, or risk controls. GenAI introduces entirely new capabilities: from drafting credit memos, generating risk scenarios, or producing customer-ready summaries—based on unstructured inputs and contextual understanding.

• Contextual Risk Analysis
  Instead of simply scoring credit applications based on rigid models, generative AI can analyze complex and evolving borrower contexts—such as the implications of supply chain disruptions or geopolitical events—then present insights in plain language to support human judgment.
• Intelligent Document Analysis and Smart Document Interpretation
  Summarize long-form complex documents—such as annual reports, balance sheets, legal disclosures or business plans—within seconds. This helps credit analysts identifying key red flags or missing information and focus on judgment rather than data extraction, drastically shortening underwriting timelines.
• Dynamic Policy Interpretation and Application
  Instead of manually interpreting credit policies, GenAI can be trained to incorporate regulatory and bank policies to match client profiles to applicable rules and flag policy exceptions or highlight relevant risk considerations—speeding up reviews and minimizing rework.
• Automated Credit Memo Generation
  Dynamically create first drafts of Credit memo summarizing the the loan applications key information. This reduces the workload for credit analysts and shortens turnaround times without compromising accuracy. 

To deploy Generative AI responsibly, Banks must embed strong governance frameworks to monitor AI outputs, validate models regularly, and provide human override mechanisms. These safeguards ensure decisions remain fair, auditable, and aligned with both internal policies and evolving regulatory expectations.

As Generative AI becomes more accessible and enterprise-ready, it presents a unique opportunity for banks to empower frontline and credit teams with on-demand credit and risk intelligence, allowing to scale lending operations without scaling headcount.

Conclusion

Having already worked with multiple clients across the financial sector to bring these capabilities to life, we can confirm these transformations are not just a theoretical, but well underway. It is those who seize the momentum today who will be best positioned to face the future.