In the rapidly evolving lending landscape, caused in part by the bank failures of 2023, credit portfolios are facing significant stress and heightened challenges, including rising default rates, fluctuating interest rates, and economic uncertainty. Coupled with strict regulatory demands for risk differentiation and portfolio diversification, these pressures are exposing the limitations of current credit portfolio monitoring processes, which are often static, reactive and subjective. The Australian CRE market has seen an 8.5% decline in capital growth with the office sector being a major contributor to this decline due to its cumulative loss of 22%­­. As a result, financial institution (FI) leaders are rethinking their credit portfolio management practices.

Artificial Intelligence (AI), particularly Generative AI (Gen AI), has transformed industries in significant ways, and financial services is no exception.Today, financial institutions (FIs) grapple with a myriad of challenges. These include time-consuming loan processing, disparate data sources, resource-intensive loan origination processes, and the overwhelming task of interpreting large sets of data.At the same time, AI is unlocking new value streams for FIs, especially in the areas of efficiency and productivity, deal pricing and profitability optimization, credit risk management, compliance, and customer experience.

While banks are partnering with innovative and forward-thinking tech vendors to improve their business and services, not all of these partnerships are successful. Even with impressive products and glowing reviews, some tech vendors aren’t suitable matches for the banks they partner with.

In the dynamic world of finance, managing risks effectively is not just a necessity—it's a hallmark of successful financial institutions.

How do you choose when to buy a solution and when to build your own in house?

In an era where speed and efficiency are key, the banking sector has continually sought innovative solutions to streamline operations and enhance customer service. One revolutionary milestone in this journey is the integration of Artificial Intelligence (AI) and machine learning (ML) in credit decisioning. This shift has redefined the landscape of banking, transforming how institutions are able to assess creditworthiness and manage risk.

It is possible to implement technology in your bank while maintaining the human touch. Commercial bankers know their business is different to retail.

The introduction of Apple’s transformative product in 2007 was a moment that changed the future. Like the now iconic iPhone, the launch of generative AI tools like ChatGPT and Google Bard have captured the public’s imagination, launching an explosion of applications and experiments across nearly every industry, from business and commerce, to art and government.For many of these use cases, the “how” behind the AI model isn’t particularly relevant. If you ask ChatGPT to compose a wedding invitation in the form of a Shakespearean sonnet, you don’t necessarily need to understand how AI can instantly conjure the right rhyme scheme.A wedding invitation is one thing; a financial institution’s credit lending decision is quite another. When it comes to such high stakes decisions, understanding the inner workings of the model is vitally important—even required. For those use cases, explainable AI is the solution.

nCino is proud to announce the release of our first Environmental, Social and Governance (ESG) report. This report delves into our journey towards a more sustainable business, affirming our commitment to responsible practices that align with our mission of transforming the financial services industry through innovation, reputation, and speed.