LendingMetrics Enhances ADP with Universal Risk Model Adaptor for Python and SAS

LendingMetrics has launched a Universal Risk Model Adaptor for its Auto Decision Platform (ADP), enabling credit risk teams to integrate models written in Python, R, SAS, PMML, and JavaScript. This update allows lenders to leverage existing predictive models within a unified decisioning engine, streamlining KYC, fraud prevention, and affordability assessments across diverse credit verticals.

What was announced

The introduction of the Universal Risk Model Adaptor marks a significant technical expansion for the Auto Decision Platform (ADP). While ADP has historically operated as a "no-code" environment—allowing risk professionals to build and manipulate logic without manual programming—this update introduces "native inclusion" for several major external coding languages. Specifically, the platform now supports Python (both Version 2 and Version 3), SAS, R, PMML, and JavaScript.

The upgrade is designed for financial institutions that have already invested heavily in bespoke risk models developed in these specific languages. By using the adaptor, these models can now interact directly with ADP’s full range of features rather than operating in silos. This functionality is aimed at a broad spectrum of the lending market, including retail banking, commercial lending, car finance, and the mortgage sector.

The goal of the integration is to simplify the management of complex decisioning logic by allowing credit risk teams to manage a single platform. Risk teams can now combine their proprietary, code-heavy predictive models with ADP's real-time decisioning capabilities to address multi-faceted requirements such as credit risk scoring, anti-money laundering (AML) checks, and detailed affordability calculations within a single interface.

"over the past 5 years we have had fantastic feedback from our clients and the industry. This has translated into our impressive commercial success, however, we are always listening to the emerging needs of our clients and now that we have completed this enormous project to deliver these additional integrations, I know that they will be delighted to have this added functionality at their fingertips"

Claire Januszczak, Head of Sales at LendingMetrics

The companies involved

LendingMetrics is a specialist in credit risk technology, providing automated decisioning solutions to the financial services sector. Based in the UK, the firm has established itself as a key player in the credit technology market, focusing on high-speed data orchestration and automated underwriting. Its flagship product, the Auto Decision Platform (ADP), is positioned as a versatile engine capable of handling the high-volume requirements of modern lenders.

The company operates in a space that demands both agility and regulatory compliance, providing tools that allow risk managers to adjust lending criteria and fraud filters in real-time. By offering a platform that traditionally focused on "no-code" compilation, LendingMetrics has sought to reduce the reliance of credit risk departments on internal IT teams. Its client base spans from niche car finance providers to established building societies and commercial lenders, reflecting the platform's scalability across different risk appetites and regulatory frameworks.

What FF News has reported before

FF News has closely followed LendingMetrics' expansion and its growing influence in the automated lending space. In 2026, the company demonstrated its market standing by securing its seventh consecutive Technology Partner of the Year win. This period of growth was supported by strategic leadership changes, including when LendingMetrics appointed Jamie Land as Head of Presales to further drive innovation in credit risk.

The platform's practical application has been highlighted through various industry partnerships. For instance, Chorley Building Society went live with ADP to modernize its digital lending operations. Additionally, FF News reported on how Camco Finance partnered with LendingMetrics to automate its specialist vehicle lending processes, showcasing the platform's versatility across different asset classes.

What this means

This move highlights a growing tension in fintech between "no-code" simplicity and the sophisticated requirements of data scientists. By opening a no-code platform to Python and R, LendingMetrics is acknowledging that high-tier lenders will not abandon their custom-coded models for a purely visual interface. The industry is moving toward a hybrid model where the speed of no-code deployment must coexist with the depth of traditional data science. This puts pressure on legacy decisioning engines that remain closed ecosystems. The challenge for the sector now is ensuring that these "native inclusions" do not introduce the very integration delays that no-code platforms were originally designed to eliminate.