Improved NPL Collection Rates for Prelios

About Prelios

One of the leading Italian non-performing loan platforms with a proven track record spanning the last thirty years, the Prelios Group is a pioneer in alternative asset management, credit servicing, and integrated real estate services. The company has emerged as one of the most prominent European NPL investors and providers of distressed asset management services to financial institutions across the continent.

With a staggering 40 billion euros in assets under management (AUM), 700+ employee and affinity for innovation, the company is positioned to have a detailed vision of industry trends and growth forecasts that assists Prelios in its decision- making processes, investment strategy planning and the mitigation of risk.

The Challenge

As banks continue to de-risk and offload non-core loan portfolios, including non-performing loan (NPL) portfolios, numerous transactions have been closed on the distressed loans market over the last decade.
The increased supply of non-performing loans portfolios gave rise to financial investors seeking out new investment opportunities in the NPL asset class, as well as to specialized debt collection companies carrying out actual debt collections.

Within the NPL universe, unsecured retail loans to private individuals are typically those transacting at a lower price points due to no collateral and higher statistical write-off rates. These portfolios typically depend on the depth and quality of contact data, with the likelihood of loan recovery being positively correlated with reachability. As a leading Italian credit servicer, Prelios was no exception. With their high value of NPL portfolios under management and the looming challenge of contact validation, the risks of inaccurate data were all too real as invalid contact data could lead to failed collections, misguided strategy decisions, and significant financial losses.

While aiming to achieve optimal loan collection results for unsecured retail loans, Prelios faced great challenges in confirming the validity of contact data and reachability of phone numbers associated with their recovery cases.

The Solution

Inhouse credit collection teams and specialized NPL servicing companies typically rely on numerous data sources to verify contractual, credit registry and real-estate data. However, when recovering unsecured loans to private individuals, these existing sources often fall short of actually verifying that contact data on file is valid. As reachability is the only intrinsic “collateral” these portfolios have, verification of contact data is of utmost importance.

As a way to enhance their loan collection processes and validate reachability, Prelios used Trustfull’s passive identity verification solution powered by digital footprints. The algorithm combines digital signals from various sources to paint a more complete picture of customer data validity and implied risk level, allowing Prelios to make better decisions.

Trustfull’s powerful identity scoring algorithm can reliably spot irregularities in customer information by assessing digital signals such as:

• Phone number status
  Indicates if the number is valid and connected
  to the network (and reachable)
• Carrier Detection
  The current network operator name of the
  inspected phone number
• Disposable number check
  Flags single-use disposable numbers available
  on the black market
• Porting history
  Indicates if the number was ported from its
  original network to a different network
• Connected chat apps
  Shows in the number is registered with
  popular messaging apps
• Connected accounts
  Shows if the number is used on popular
  online marketplaces and services

When collated together, these data signals paint a risk-picture about the phone number Prelios has on file, allowing them to identify reachable numbers where contact is likely to be established with the debtor.
In the opposite case, Prelios can now be instantly aware of cases where reachability can’t be ensured due to low phone score, and process these cases differently in accordance with their policies.