Risika Credit Automator

The Problem
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Larger organisations with diverse distribution channels and multiple customer segments spend large amounts of time managing credit decisions. These decisions are integral to the overarching objectives of mitigating credit risk and ensuring regulatory compliance across their business operations.

Finance and Sales teams often find themselves overwhelmed by the complexity of credit policies and the burden of repetitive manual tasks required to grant customers credit. The manual credit decision-making process is characterised by its sluggishness, time-consuming nature, and susceptibility to human errors. These challenges not only frustrate internal teams but also result in customer dissatisfaction.

Customers follow a standardised process for creating, managing and updating credit policies, typically on an annual basis.

Key Pain Points‍

Credit policy compliance
Sales and Finance struggle to align their objectives, making it challenging to communicate credit decisions effectively. Static documentation hampers understanding, resulting in time-consuming engagement. Misalignment leads to generic decisions, necessitating significant effort to clarify and tailor policies.

Human error
Sales frequently misapply credit rules, causing inconsistency and misunderstandings. Updating policies often lacks transparency and causes confusion, leading to time-consuming customer data reviews for Finance teams.

Fraud
Detecting fraud proves difficult and time-consuming, contributing to a reactive mindset. Despite awareness, proactive detection remains a daunting task, reinforcing a payment-centric approach and accepting fraud as inevitable.

Policy updates
Updating credit policies, especially across diverse channels and customer types, is a time-consuming ordeal. Failure to adapt to changing conditions without analytics hampers policy optimization, compelling Finance teams to maintain the status quo until external events force changes.

The Product
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The product introduces credit automation, saving teams thousands of hours on credit decisions. Users can control their credit limits by setting their own limits, or relying on Risika’s recommendations. With Credit Automator, setting up credit automations for specific groups of customers is easy.
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A credit policy consists of several rules and variables the user sets. We designed an intuitive interface, where constructing credit policies is as simple as connecting modular nodes. Each node represents a rule and the user connects these nodes, forming decision trees with YES or NO branches. This visual approach provides transparency and flexibility, enabling users to adjust their policies on the fly.

The nodes are always visible at the centre of the screen, ensuring users have a clear view of their decision structure. We make use of side panels to host various interactions to specify different rules and variables that make up a credit policy. These action panels are primarily composed of form elements like input fields and dropdown selects.
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Users can evaluate the impact of their policies on customer lists, allowing for adjustments to fine-tune their credit strategies.

After the policy is published, it automatically handles approvals, rejections, and manual checks according to the established rules. The policy outcome widget assists sales teams in comprehending customer credit limits and identifying the rules that influenced the credit decision.

UI Components
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At the time this project kicked off, I was creating a design system for the Risika dashboard and customising Material-UI (MUI) components by theming, a separate project detailed here. However, Credit Automator had unique requirements and the MUI library was not going to cut it.
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In response to these specific needs, I started designing a bespoke library of custom UI components from the ground up. These components not only addressed the immediate demands of Credit Automator but also seamlessly integrated with our overarching design system, ensuring consistency and cohesiveness throughout the Risika dashboard.
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Usability and accessibility took centre stage in these component designs. The complexity of certain tasks and components, such as a multi-layered dropdown select, presented a prime opportunity to apply best form field practices. The freedom afforded by crafting custom components allowed me to optimise user interactions, prioritising a seamless experience for all users.

The majority of participants found the remarkable level of automation, flexibility, and ease of use to be a significant enhancement to their existing credit processes, igniting their enthusiasm for Credit Automator: