Delphi Dominates Analytics
Delphi is, at it’s core, built around an understanding of analytics. Data can come in all sizes and types; big-data – with it’s unique challenges and tools, medium-sized data – the old style data-base access we used to think were big, and unstructured data – the types of data that drives us crazy. Delphi does data – all types.
Project- or Subscription-basis
Delphi takes deep-dives into data, on either a project basis (with a limited scope or duration) or on a subscription basis (we provide an on-going service, with repeated flows and reporting). We’ve undertaken projects with all types of data in strange and wonderful markets – and we’ve taken subscription projects that are decades in duration. We love challenges.
In general Delphi’s analytical services fall into one of the following categories:
- Litigation support
- Subscription-based reporting
- Financial modeling
- Machine learning / Artificial Intelligence
- Complex-Network and Community analytics
- Data supplements
These are explained below.
Delphi has been called upon to support major litigation among counterparts at the highest level. Global institutions and government regulators have called upon Delphi to act as expert witness and quantitative ‘story-teller’ in complex litigation scenarios. If there is a story that can be told from data, then Delphi is the party to find and tell that story with vivid detail.
Add Delphi to the litigation support team for confidence in understanding the data narrative.
Delphi can arrange for regular, complex flows of data to run through sophisticated analytics to provide regular management and oversight reporting.
- We do regular monthly or weekly reporting for customer valuation, for segment statistics, for marketing effectiveness measures and for collection and call-center effectiveness.
- We provide regular collection oversight for loan and receivable servicing (as master servicer and trust reporting entity).
- We provide Statistical Process Control and management reporting.
- We do regular NLP (Natural Language Processing) analysis of unstructured data (e.g., customer sentiment).
Delphi builds reliable, auditable financial models for:
- Asset valuation and balance-sheet management / capital allocation,
- Development of reporting process for regular ‘master servicing‘ of assets,
- Risk management models and model-risk management validations.
Machine Learning – Artificial Intelligence
Delphi is a great team member for identifying opportunities to implement AI (Artificial Intelligence) and machine learning processes into a business flow to constantly improve processes.
This is often done by examining critical input values and measurable outcomes of business process for either internal process improvement, or for decision-making on customer relationship management issues. Which customers should you promote or treat with special care, or which customers are financially unattractive to your business? Delphi can help you answer these questions.
As part of a regular, AI-based regime for constant improvement, Delphi is a great partner for creating and implementing Champion / Challenger testing for assessing when and with whom processes and decision-making rubrics.
Complex-Networks and Community Analytics
Delphi has specialized in complex networks since early in the 2000’s. These are networks of things – called ‘nodes’ that might be people, for example, which are linked together in various ways. People that are linked as Facebook friends, or that follow each other on Twitter are examples. Sometimes these networks are derived from phone call or text message data streams – or from e-mail contacts or from appearance at common events or on common lists, etc. There are many sources of complex-network data.
With data streams that comprise complex-network information, Delphi has been able to make significant contributions to insight based upon what we call ‘Community Analytics.’ Communities are the various groupings of people, for example, with tight clusters of common contacts.
Delphi has used complex-networks and community analysis in fraud network detection – and in identifying risks associated with network-proximity to clusters of other risky nodes. The common features of a social circle, for example, are often predictors of common behavior.
This general line of work has been called social-network analytics and it has been very useful for predicting common behaviors of groups. This includes marketing success, as well as contributing factors to credit scoring.
From a practical consideration, then has been very useful in such mundane efforts as skip-tracing in sub-prime auto finance populations.
Delphi gained a particular niche in the credit modeling world in the early 1990’s with its introduction of varied data supplements to credit scoring models.
Delphi’s machine-learning and behavioral modeling tools (e.g., the MANN model) allowed Delphi’s credit models, for example, to include many hundreds of variables. Models were enhanced as relevant data were added to the trove of information known about customers. Initially, those data supplements consisted of publicly available data (such as Bureau of Labor Statistics employment information by locale). But, these were soon expanded to incorporate things like magazine subscriptions and other affiliation data.
Recent proliferation of GIS (Geographic Information System) data allows models in boutique- and developing-markets to include crop information, water availability, proximity to transportation networks, etc. Delphi has established global systems of GIS data for supplementing behavior models in markets such as India, Myanmar, African states, and so forth.
Additionally, there are often ‘derivative’ data from a client’s own data sets that provide custom supplements that might not normally be considered as fields of use in behavior modeling. Delphi has maintained a position on the cutting edge of incorporating diverse sets of supplemental information in modeling.