Financial risk technology

The challenge

Financial risk management across market, credit and liquidity risk in most organizations is becoming a high cost and key pinch point area within the organization.

Waves of regulation have consumed change budgets, however, these have not always paid for the next generation of risk technology and data analytics platforms to be implemented.

Many regulations directly affect data, analytics and technology given their focus on risk data quality, consistency with financial data, methods of aggregation and reporting, and related processes. Many institutions are still scrambling to meet them given limitations on the current legacy risk architecture.

This has resulted in high manual effort employed in the risk functions based around the production of information e.g. regulatory reports, risk reporting that has led to unnecessarily high labour costs.

Some of the current challenges that organisations face in these areas are as follows:

Technology

  • Manual business activities: multiple handoffs and use of EADAs creating inefficient labour intensive risk processes
  • Poor data quality: leading to inaccurate outcomes, poor accuracy of MI and risk monitoring
  • Disconnected data: point to point data sourcing with few golden source and an inability to make data joins across multiple domains, making it difficult to provide holistic risk monitoring
  • Risk analytics: risk analytics and MI run on multiple platforms carried out within data silos
  • Segregated systems: duplicate capabilities across functional areas leading to a complex IT landscape, limiting ability for change, collaboration and innovation across the three lines of defence

Change Management

  • Inadequate budgets based on poorly considered Risk Digitisation business cases: leading to stress, reduction in change elements delivered, and programmes falling short of expectations
  • Leadership and sponsorship: the CRO, CDO, Chief Digital Officer and CTO roles within firms are often conflicting and getting these groups working is crucial for risk digitisation to be achieved
  • Habit, fear and culture: prior habits can be a powerful constraint which impede change because staff who stick with a prior way of doing things may not adopt a new way and technologies
  • New talent required in emerging technologies, collaboration and agile ways of working: new resource profiles will include data scientists who have advanced mathematical and statistical knowledge and are experts in machine learning and other sophisticated data-analysis methods

In addition to these challenges, important technology trends are now gathering momentum, suggesting that the risk management operating model will experience substantial change in the next decade. Advances in technology, change management and ways of working are positively impacting organisations ability to implement risk digitization and its associated benefits such as reduction in FTE costs, lower capital and meeting regulatory and compliance requirements.

Firms need to use a number of technology levers to automate and improve the current risk architecture from Application simplification, movement to cloud, advanced analytics and AI adoption.

The key enablers to consider are:

  • Re-architect using SOA: de-couple business services from technology and expose services through SOA for greater architectural flexibility. Move to micro service for Cloud enablement.
  • Implementation of Mature Risk Data Management: the foundation of mature Risk process is effective Enterprise Data Governance across the process lifecycle: build on the work done as part of the regulatory agenda to address fragmentation and embed appropriate taxonomies, standards and governance to significantly improve data quality
  • Leverage Cloud and optimise infrastructure: migrate relevant services to cloud infrastructure with resulting impact on cost and technology flexibility. Exploit greater compute power for more timely risk processing
  • Optimise Risk insight through analytics, machine learning and AI for simulation/model development: augment and/or replace human risk decisioning with supervised learning models and optimise risk development/simulation via machine learning
  • On demand in-memory analytics and risk monitoring: ability to analyse the data across many dimensions and get more quick and efficient insights into the risk profile of the bank at any given point.

Our services

We work with clients to implement digitization opportunities across financial risk activities. We do this through the delivery of improved risk architecture strategies and the implementation of risk management technologies for automation and benefits realisation. We combine deep risk technology expertise with our business domain capabilities in data and risk model governance to ensure the future design is fit for purpose. We provide services that will assist in solving problems across our clients’ entire Risk value chain such as:

Risk IT strategic architecture roadmaps

  • Design of ‘best in class’ risk technology platforms across traded, market, credit, and liquidity risk along with implementation roadmaps and technology standards and principles
  • Vendor capability assessments, selection and management of vendor implementation programmes

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Migration of risk applications to the cloud

  • Advise banks on the suitable cloud infrastructure and strategy (public and private cloud)
  • Risk application transformation for migration to the cloud (grid, GPU technology and containerisation)
  • Architecture and optimisation of applications for optimal cloud performance

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Risk control and monitoring analytics

  • Design of risk monitoring platforms and control rooms to deliver real time credit/market/liquidity alert generation
  • Multi analytical techniques including AI, social network analysis, self-learning models and predictive modelling
  • Detailed data architecture design to join multiple data domains and achieve holistic surveillance to reduce the level of false positives, risk oversight and investigation management effort in 1st and 2nd line

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Implementing advanced AI capabilities for risk management

  • Assisting clients to reduce the cost of risk model build using advanced AI and machine learning techniques for model development, simulation and stress testing environments
  • Build of machine learning models with audit trails on the model assumptions, inputs and hierarchies (model interoperability) to enable risk methodology, internal audit, and regulators to understand the model and approve usage in production systems

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Risk data governance and data model design

  • Implementation of effective data management frameworks for risk analytical data to enable banks to enhance their regulatory and business reporting, such as Basel Committee on Banking Supervision (BCBS) 239
  • Design of data definitions / taxonomies, enterprise data models, data usage patterns and KRI’s to enable aggregation of risk data across multiple data marts, data warehouse and risk calculation engines
  • Data quality design for each level of the risk architecture, ensuring that calculation engines/risk processes operate properly with quality-controlled data

 

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Contact us

James Morgan

Partner, PwC United Kingdom

Tel: +44 (0) 7776 180 836

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