QUALCO DATA-DRIVEN DECISIONS ENGINE (D³E)

Automate data ingestion and due diligence

With QUALCO Data-Driven Decisions Engine’s you can eliminate the time and effort spent just getting access to the data, by using configurable schema to adapt to any portfolio’s data model as delivered. You can also build and reuse collections of data quality and business logic checks, at both granular and aggregate levels. Optionally build and reuse a library of transformations to convert any portfolio’s data into your preferred data model, to maximise the reuse of all the analytics assets you build within or outside the software

Segment, stratify and aggregate data

Use QUALCO Data-Driven Decisions Engine’s configurable multidimensional data repositories to produce standard reports or aggregate data sets that feed directly into your existing per-segment valuation models and business plans. Easily monitor the performance of your models versus actual outcomes, and extract data to update your models’ assumptions and parameters

Produce data-driven valuation models

With QUALCO Data-Driven Decisions Engine’s documented best-practice approach you can build granular, machine learning-based valuation models that predict a portfolio’s recoveries without making assumptions about the behaviour of specific segments. Build a library of such models on top of your preferred data models and reuse them on new portfolios

Establish ground truth

Exploit QUALCO Data-Driven Decisions Engine’s data repositories and access to reliable portfolio data, updated daily and reflecting actual operations. Use the full, granular history of the portfolio’s evolution, to identify what works and what doesn’t. Reach a wide consensus on the above so that the entire organisation remains focused on its goals and on the best ways to reach them

Identify innovative strategies

Leverage the predictive modelling capabilities offered by QUALCO Data-Driven Decisions Engine’s to sort through the endless array of choices involved in developing your portfolio treatment strategy. From segmentation, to choose of Collection Agencies and communication channels, to repayment plan options, it will identify key drivers of customer behaviour, highlighting problems and opportunities that can inform your strategy, as well as delivering actionable predictions to help with its implementation

Produce data-driven valuation models

With QUALCO Data-Driven Decisions Engine’s documented best-practice approach you can build granular, machine learning-based valuation models that predict a portfolio’s recoveries without making assumptions about the behaviour of specific segments. Build a library of such models on top of your preferred data models and reuse them on new portfolios

Provide clarity and transparency

• Build any number of predictive models – from the essentials, like probability of default or expected recoveries, to more specialised ones, like the best time to call, the probability to accept a payment plan, most suitable solution and more
• Use the models to identify key characteristics, as well as extract granular predictions, that foresee a portfolio’s recoveries without making assumptions about the behaviour of specific segments. Build a library of such models on top of your preferred data models and apply them to new portfolios
• Feed the predictions automatically to your operations systems to automate the corresponding actions within your workflows and monitor the effectiveness of such workflow changes at every stage – from A/B tests to full rollout – and establish an ongoing optimisation loop under your full control

Share information while maintaining control

Make use of the metadata and access control configuration to share reports or results of analyses securely across the organisation or even outside it – with investors, Collection Agencies and other business partners. Establish a common language with all stakeholders and achieve consensus on treatment effectiveness

Enrich the portfolio data model

Use QUALCO Data-Driven Decisions Engine’s configurable transformations engine to build, verify, sequence, maintain and reuse calculations, eliminating “data silos” and extensive data model impact analysis

Ensure consistency

Make sure reports agree with each other; keep track of the provenance, definition, and evolution of any data item throughout your data processing chain

Maintain an open data ecosystem

Provide self-service capabilities as well as predefined reporting deliverables through any data feed technology or reporting front- end of your choice. Deal easily with ad hoc requests through reuse of any existing analyses and automate periodic data exchanges with regulators, credit bureaus, and other data consumers