Diverse business people having a casual meeting together in an office

How SME lenders can build next-generation credit decisioning

Discover the key factors and design principles for a framework that improves speed, quality, and customer experience.

In brief

  • Transforming credit decisioning presents significant opportunities and benefits for lenders that can navigate the complex process.
  • Lenders need to develop better decisioning capabilities that harness technology, analytics and richer data.
  • Almost half of SMEs now demand faster access to borrowing.

Banks and other lenders are increasingly seeking to transform their Small and Medium-sized Enterprises (SMEs) lending businesses amid mounting competitive pressures, including from incumbent market players and fresh entrants, heightened customer expectations and unprecedented opportunities presented by advancements in data technology and analytics.

A major part of this transformation involves a radical reshaping of the methodologies in which firms make credit decisions, measure and manage risks, and support customers across the end-to-end credit lifecycle. This reshaping pivots on two key objectives:

  1. To improve profitability through consistent, high-quality lending decisions and lower acquisition costs.
  2. To manage the credit risk of existing customers more proactively.

Successful delivery of both objectives depends on how well the firm leverages the collective power of advanced analytics, big data and digital technology - not just at the inception point of loan origination but also for ongoing customer management.

In this article, we explore the complex landscape of contemporary SME lending and highlight:

  • The key drivers behind this significant transformation
  • The principles that underpin the next generation of decisioning frameworks for SMEs, as well as the critical success factors required to deliver them
  • The importance of taking a coordinated approach across the business and the risk function

Chapter 1

Five forces driving credit transformation

Evolving customer needs, easier market entry and heightened regulation are disrupting SME lending.

Changes in SME lending are creating significant competitive opportunities and challenges that are fundamentally reshaping the marketplace:

1) Emergence of FinTech and Big Tech lenders

SME lending markets face pressure from new entrants, including FinTech lenders, while incumbent players are sharpening their focus on SME lending. Competition is fierce to offer the best customer experience and optimize product features and pricing. A global EY survey of 6,000 SMEs shows the growth in those seeking alternative financial service providers, with 31% of SMEs indicating they would consider a FinTech provider and 26% a big tech provider as a source of financial means.

2) Changing expectations

SMEs are becoming significantly more demanding of their lenders. Our survey highlighted that 48% of SMEs are interested in faster credit as a service. This is driving lenders to simplify and streamline credit policies and extend automated and assisted decision-making.

3) Richer, more accessible data

Lenders of all sizes now have opportunities to access new sources and unprecedented volumes of real-time customer data packed with rich insights. While not without its challenges, the potential upside of transactional data alone is huge, and an entire ecosystem of service providers exists to support this.

4) Technology is better and cheaper

Scalable and efficient technology solutions are driving extensive digitization and automation across customer journeys, delivering enhanced support for customer interaction through artificial intelligence (AI) assistants. This is lowering barriers to entry for some but also leaves incumbent lenders with significant challenges to turn their legacy approaches into future-ready solutions.

5) Heightened regulatory scrutiny

A steady stream of regulatory and legal changes, including Consumer Duty, lending regulations (e.g., CONC in the UK), and open banking/open finance, creates challenges in understanding and applying the impacts on existing business practices and raises the cost of compliance.

2021 EY SME Global Survey



Chapter 2

Four key design principles for next-gen credit decisioning

Streamline approval processes with smart automation, simplified policies and effective controls.

A “next-generation credit” decisioning framework for SME lending should be built around the following key design principles:


1) Maximize the use of data to generate customer insights

SMEs seek long-term relationships with their banking partners and expect them to understand their unique business challenges. Traditional decisioning frameworks have typically relied on outdated financial data which has limited the ability to accurately assess customer risk, particularly the "thin-file" population. Transaction data analysis offers much richer insights, is almost real-time, and - thanks to Open Banking - widely accessible even when the primary banking relationship is elsewhere. There is also increasing focus on harvesting the wealth of unstructured data from the application process itself. Customers clearly recognize the potential benefit, with 82% of SMEs interested in sharing their data with their bank for better services. The power of this data comes from augmenting them with traditional sources rather than replacing them.


Lenders face challenges with data volume, legacy approaches, and how to maximize the value of the insights from new and existing customers. Newer lenders and those with a clear vendor strategy typically have an advantage in this area, yielding better quality and automation of decisioning.


2) Automate to the optimal level

Smart automation, powered by richer data, is key to delivering the fast, streamlined digital lending experience SMEs desire, with some even willing to pay extra for speed.


To achieve intuitive, transparent, and reproducible auto-decisioning, deeper customer insights are essential, allowing for more up-to-date risk signals and new criteria for auto-reject rules. To fully reap the benefits, a broader process transformation is needed, with the quality and consistency of manual underwriting also enhanced through auto-collation of customer insights, pricing intelligence, and intelligent decision tools prompting next best actions. This can help drive down costs and improve operational resilience and scalability. Implemented well, a structured auto-decisioning engine can drive up straight through processing approval rates, lower operational expenses and increase wallet and market share from the top customers.


But while lenders are setting higher targets for auto-decisioning, it is important that decision quality, customer experience and cost efficiency are not sacrificed.


Automation also adds value beyond decisioning, from automating data ingestion to supporting post-sanction fulfilment, though SMEs have been clear that they still expect a human-led approach in customer-facing processes.


3) Simplify credit policies and align to strategy

To stand out and attract and retain creditworthy clients, banks need to build focused, customer-centric strategies and offer a customized suite of products that match SME requirements. Transformation of decisioning is most effective when it starts with a clear business vision and lending strategy that can be translated into a streamlined credit policy aligned with risk appetite.


Many larger lenders operate in a complex policy landscape and are making significant efforts to streamline and modernize this process. From a decisioning perspective, this includes simplifying requirements to remove unnecessary friction in the approval process and enabling more dynamic segmentation based on cross-product risk characteristics and risk drivers to maximize flow through auto-decisioning pathways.


Flexibility to update and deploy credit policies is also a design requirement. The pandemic experience highlighted to many lenders a need for greater agility to enable the business to pivot when needed.


Regulatory scrutiny is also increasing, which raises the risk of unforeseen changes. The next generation decisioning framework needs to be able to adapt to the evolving commercial and risk priorities of the bank, such as sector or product strategy. Lenders need to consider how to future-proof their framework as part of any transformation effort. For auto-decisioning, this could involve designing a system that supports expansion to higher thresholds and across products through a set of ready-to-go model blueprints, including critical risk drivers, weight mix of variables, and model performance. Generally, a greater level of standardization across processes (while retaining the ability to adapt and tailor offerings) combined with well-designed, modular technology solutions enables greater agility.


4) Embed effective controls and governance across the end-to-end credit cycle

Deploying credit strategies effectively and managing increased volumes of customer data present new control challenges across the credit cycle. The use of new data and analytics, especially Machine Learning techniques, requires appropriate governance to maintain transparency and trust. Decision criteria must be understandable and explainable. Strong and clear governance is necessary to provide clarity on when manual intervention is required and when full automation is the goal.


Compared to traditional models, extra care is needed to avoid unintended consequences, such as selection bias resulting from data and model training methods. A robust update approach is also necessary, where recent customer experience is used to recalibrate models and decision-making approaches at an appropriate frequency and implemented in a well-controlled way.


Most banks have been slow to adapt to these requirements, but it is clear that regulatory authorities are monitoring this area so lenders should also make it a focus.

Next generation decisioning framework


chapter 3

Four success factors for a rapid transition

Lenders that can quickly transform decisioning frameworks can gain a competitive advantage.

Transforming decision frameworks is a complex task involving multiple departments. These four success factors can facilitate the process:

1) Transformation of decisioning should not be done in isolation

Credit decisioning is a critical part of effective lending, and deeper customer insights are vital to its transformation. These insights are derived from data and delivered through technology that has much broader potential. It's important to consider how this can benefit the entire credit lifecycle and lines of defense. Enabling real-time credit risk early warning and credit review automation are obvious areas, but there are further opportunities.

There are also potential consequences that must be managed, such as changes to the lender’s risk profile that may require changes to risk management approaches, and inconsistencies in information used by different stakeholders in the organization.

Failure to consider impacts across the credit lifecycle can result in missed benefits and unintended consequences.

2) Do not underestimate the challenges in transforming legacy approaches

Embedding new credit assessment and decisioning techniques is challenging due to managing various data, deploying decision analytics, combining legacy and next-gen techniques and upskilling employees. Despite these challenges, it's important not to use them as an excuse for inaction, considering the significant benefits involved.

3) A well-defined and executed vendor strategy can save time and money

Lenders must choose whether to invest in-house or use third-party vendors. A good vendor strategy can speed up business delivery and reduce costs. Consider using vendors for sourcing and enriching transaction data given the overheads involved in doing this in-house. Also, identify potential ecosystem partners during digital transformation to assess the potential benefits they could bring.

4) Collaboration between business, risk and delivery stakeholders is critical for delivering business outcomes

For effective decisioning transformation, a strong partnership between Business, Risk and Transformation Delivery stakeholders is crucial. Agile approaches, multi-skilled delivery pods and effective planning and governance can help manage costs and provide an early view of outcomes. A robust and well represented design authority helps navigate trade-offs and compromises while ensuring alignment to the transformation vision while.

This article is co-authored by Liam Mackenzie, Director, Business Consulting, Ernst & Young LLP and additional EY contributors include Gorden Mantell, Partner, Financial Services, Ernst & Young GmbH Wirtschaftsprüfungsgesellschaft; Anna Kozuchowska, Partner, Risk Consulting, Ernst & Young spółka z ograniczoną odpowiedzialnością Consulting spółka; Nimilita Chatterjee, Partner, Financial Services Risk Analytics, Ernst & Young LLP; and Sonja Koerner, Partner, Financial Services Consulting, Ernst & Young LLP.


The evolving needs of SMEs are dovetailing with the technology and analytics required to meet them. Real-time insights, automation, and effective credit policies and controls are crucial for navigating this complex journey. Lenders that can develop a credit-decisioning and customer management framework supported by advanced analytics, big data, and digital technology can benefit from consistent, high-quality lending decisions, lower acquisition costs, and proactive credit risk management.

About this article


Related content

How a new technology platform can help banks unlock financing for SMEs

The Cashbot platform offers banks an online invoice finance solution for SME clients.

12 Oct 2023 EY Global

How a global FinTech captured growth in the SME segment

A global Fintech captured growth in an opportunistic SME segment with a differentiated, holistic strategy. Learn more in this case study.

14 Sep 2023 Matt Cox + 2

Why digital lending is the future for banks and SMEs

Banks can serve and understand small businesses better through a modern digital credit process that is data-led and can deliver funds quickly.

22 Jul 2022 Filippo Mastropietro + 1