NEAT EVALUATION FOR Coforge: RPA & AI in Banking

Market Segment: Overall

Introduction

This is a custom report for Coforge (Coforge) presenting the findings of the NelsonHall NEAT vendor evaluation for RPA & AI in Banking in the Overall market segment. It contains the NEAT graph of vendor performance, a summary vendor analysis of Coforge for RPA & AI in banking services, and the latest market analysis summary for RPA & AI in banking.

This NelsonHall Vendor Evaluation & Assessment Tool (NEAT) analyzes the performance of vendors offering RPA & AI services in the banking sector. The NEAT tool allows strategic sourcing managers to assess the capability of vendors across a range of criteria and business situations and identify the best performing vendors overall, and with specific capability around RPA, AI, and supporting new digital banking models.

Evaluating vendors on both their ‘ability to deliver immediate benefit’ and their ‘ability to meet client future requirements’, vendors are identified in one of four categories: Leaders, High Achievers, Innovators, and Major Players.

Vendors evaluated for this NEAT are: Atos, Capgemini, CGI, Firstsource, Genpact, HCL Technologies, Infosys, LTI, Mphasis, Coforge, NTT Data, Tech Mahindra, Wipro, and WNS Global Services.

Further explanation of the NEAT methodology is included at the end of the report.

NEAT Evaluation: RPA & AI in Banking (Overall)

Coforge

NelsonHall has identified Coforge as a Leader in the Overall market segment, as shown in the NEAT graph. This market segment reflects Coforge’s overall ability to meet future client requirements as well as delivering immediate benefits to RPA & AI clients in the banking sector.

Leaders are vendors that exhibit both a high ability relative to their peers to deliver immediate benefit and a high capability relative to their peers to meet client future requirements.

Buy-side organizations can access the RPA & AI in Banking NEAT tool (Overall) here.

Save for Later Download Reports
Description More

Vendor Analysis Summary for Coforge

Overview

Coforge has been active in RPA since 2015, initially applying RPA to fund administration processes at a leading wealth management platform provider. Previously, the processes were processed manually. Coforge started a COE for the client and partnered with UIPath to identify processes and implement RPA for those processes. After the initial automation of fund administration processes, over eighteen months, Coforge extended the robotic automation into customer interaction processes, as well as reporting and general ledger processes.

Strengths

  • Strong framework and tools for automation and AI which have been applied to multiple capital markets clients
  • Solid experience in the application of UiPath
  • Willingness to charge per bot once deployment complete
  • Orchestrated solutions converging RPA, AI and BPM to deliver Smart Automation across enterprise
  • Strong AI and RPA partner ecosystem comprising of Arago, Artificial Solutions, Pega, Appian, UIPath and Automation Anywhere.

Challenges

  • Limited market awareness of their experience in cognitive technologies deployments
  • Lack of significant Blue Prism market presence.

Strategic Direction

Coforge will continue to largely target RPA and AI opportunities within capital markets and transaction-based processes within financial services. Many of the company's RPA implementations so far have been client-specific, and so Coforge is looking to template its existing RPA configurations and apply them more widely within the financial services industry.

Coforge leads with its frameworks and domain knowledge to optimize processes and then automate them. Clients are charged when bots become operational. Coforge receives the bulk of its RPA revenues from ongoing operations support so that clients are paying for service as they are receiving the benefits of that service.

The company is actively assisting clients to establish RPA COEs, which enables Coforge to become a long-term service provider to the client and help the client to scale RPA and AI within their organization. Coforge is expanding its technical capabilities by developing POCs using machine learning to make inferences based on past data in support of capital market processes and the IT service desk. Finally, Coforge is developing their automation platform using the open source code. The bots developed will use AI to enable OCR at lower price points than existing third-party RPA solutions.

Outlook

Coforge is well positioned to respond to capital markets firms, and banks demand RPA and AI engagements. It has a robust framework for process consulting and RPA implementation, a good installed base of RPA projects, a large portfolio of managed support services, and a strong pipeline for additional RPA and AI projects. Coforge has grown its RPA and AI practice in banking by ~20% per year over the last two years. It should be able to grow this practice in double digits per year for the next five years. Beyond that, to continue to grow its RPA and AI revenues in banking in the high single digits, it will need to develop aggressively:

  • As-a-service offerings based on its proprietary frameworks and partner technologies
  • Expand the number of RPA and AI product vendors it works with to deliver RPA and AI services.

RPA & AI in Banking Market Summary

Overview

The banking industry is adapting to new business conditions where they need to drive revenues from faster introduction of new products which will have lower profit margins than in the past. To deliver these products profitably will require highly standardized, consolidated, automated operations across multiple products and markets. Operations need to be able to scale up/down with a very low cost of delivery.

Drivers in the market for banking RPA and AI services include:

 

  • Cost: need to achieve operations cost reduction at all volumes without re-platforming. Compliance has been the major driver of operations cost increase (10X increase) for the past five years
  • CUX: to drive improved customer acquisition and retention
  • Need to change business models: agility, efficiency, and accuracy are needed to enable emerging models
  • Need to increase revenues to offset margin declines: improved sales/marketing campaigns, improved coordination across silos, and faster time to market for new banking products.

Buy-Side Dynamics

RPA and AI services are established with tier one banks in mature markets. Lower tier banks are beginning to consider widespread adoption due to severe cost pressures.

The primary client profile is:

  • Current: Tier 1 banks are primary adopters, but regional banks represent 20% of revenues, up from 1% 24-months ago
  • Future: Expand into regional banks, local banks, specialty/startup banks, and emerging markets. Expand into industry consortia (e.g., exchanges): support cloud delivery and industry shared services.

Clients are buying service bundles including:

  • Consulting (20%), design & deploy (50%, up from 40% two years ago) and operations support (30%, up from 10% two years ago)
  • Internal bank operations deployment 60%, cloud/SaaS 20%, and BPS delivery 20%. RPA (70% overall) and AI (30%).

Market Size & Growth

NelsonHall estimates the size of the RPA and AI Services in Banking market to be ~$635 m in 2018, and that it will grow at 14.9% per year in the period 2018 to 2023.

The RPA and AI Services in banking market starts with Consulting, which accounts for ~20% ($130m) of client spend and is growing at 10.0% over the forecast period. Design & Deploy accounts for ~50% ($320m) of client spend and is growing at 15.0% over the forecast period.

Finally, Operations support accounts for ~30% ($185m) of client spend and is growing at ~18.9% over the forecast period.

Challenges

The key challenges in the market for banking RPA and AI services include:

External challenges

  • Improving yield on use cases: low yield on POCs has created client demand for improved use case development
  • Security: increasing use of cloud environments, shared environments, and open environments
  • Ability to offer new digital process models, both in services delivered and internally delivered ops
  • Developing plugins for legacy platforms, new platforms, and business ecosystems
  • Deciding when to partner, use open source, acquire, or share products
  • AI remains a very early stage technology, far behind RPA in adoption. Key AI functionality to date is focused on customer attitude interpretation via manipulation of unstructured data. Many product vendors are likely to fall behind as the technology matures
  • Regulations: All FIs need to increase regulatory reporting and standardization of processing. The current focus is on AML/KYC/FATCA, which impacts account opening and customer onboarding and is the primary use case for RPA and AI to date
  • AI applied to transactions data is only useful in tier one banks, which have large, statistically meaningful transactions data pools to draw inferences from. Smaller banks, if they are to use this technology will need to draw from industry wide data pools. The same applies for data sets from small markets and limited bank product runs

Internal challenges

  • Combining RPA and AI into unified offerings to enhance functionality of bots
  • Managing and coordinating groups of bots and redeploy bots as operational needs change
  • Vendors need to assume project delivery risk; most clients expect vendors to retain implementation risk via pricing
  • Assessing machine learning capabilities prior to implementation to ensure QC gains
  • Implementing quality control metrics as part of pricing scheme
  • Tier one banks make each project a custom one, limiting reuse of IP. Need to increase reuse of IP
  • Talent development: RPA and AI qualifications are in short supply, and new methodologies and technologies are both in flux and not widely taught. Services vendors need to provide strong training and recruiting resources
  • Expanding addressable operations footprint: savings are very high (>50% cost reduction), but currently applicable to very few processes, limiting overall business impact. Need to penetrate further into client operations to deliver high impact to overall operations
  • Standardizing process execution: Successful implementation of RPA and/or AI requires a redesign of processes. However, standardizing across multiple markets and products remains a challenge in addressing local regulations
  • Hosting/cloud/systems integration/consolidation: small/medium banks have limited internal staff, requiring support for IT/BPS offerings. Vendors looking to expand to this market must be able to deliver automation as a BPaaS
  • Process quality: RPA/AI reduces human error and transaction rework. They do not improve process adaptation to changing industry conditions or process execution where inputs are flawed (data source is compromised). Working with suppliers such as product/data vendors is required to identify and scrub best data feeds

Success Factors

Key success factors for clients include:

  • Strategy development:
    • Developing a roadmap to achieve agile/flexible operations delivery using cloud/BPS/heterogenous delivery environment
    • Building an ecosystem of vendors and decide how each of them are to interact with client to deliver effectively.
  • Vendor selection:
    • Multiple grades of vendors include established product vendors for robust ops, emerging vendors for new functionality, and services vendor IP for low software cost/low complexity services
    • Selecting product vendors for functionality, roadmap, and financial strength
    • Preferred vendors should have the widest pool of IT services providers supporting them.
  • Execution:
    • Redefining the external/internal operations split: ability to articulate required proprietary operations (high-value, non-repetitive processes) and outsourceable operations (lower-value/less differentiation), and still integrate the two sets of processes effectively. RPA and AI deliver high cost savings, but on a small operations footprint. Enlarging the footprint is the highest cost saver undertaken by successful banks
    • Accurate selection of processes for RPA and AI (currently over 60% of POCs fail to meet business case). Use case libraries are mitigating this challenge
    • Sharing the overheads of internal operations by standardization and SSCs; and external operations by cloud delivery and BPaaS.

NEAT Methodology for RPA & AI in Banking

NelsonHall’s (vendor) Evaluation & Assessment Tool (NEAT) is a method by which strategic sourcing managers can evaluate outsourcing vendors and is part of NelsonHall's Speed-toSource initiative. The NEAT tool sits at the front-end of the vendor screening process and consists of a two-axis model: assessing vendors against their ‘ability to deliver immediate benefit’ to buy-side organizations and their ‘ability to meet client future requirements’. The latter axis is a pragmatic assessment of the vendor's ability to take clients on an innovation journey over the lifetime of their next contract.

The ‘ability to deliver immediate benefit’ assessment is based on the criteria shown in Exhibit 1, typically reflecting the current maturity of the vendor’s offerings, delivery capability, benefits achievement on behalf of clients, and customer presence.

The ‘ability to meet client future requirements’ assessment is based on the criteria shown in Exhibit 2, and provides a measure of the extent to which the supplier is well-positioned to support the customer journey over the life of a contract. This includes criteria such as the level of partnership established with clients, the mechanisms in place to drive innovation, the level of investment in the service, and the financial stability of the vendor.

The vendors covered in NelsonHall NEAT projects are typically the leaders in their fields. However, within this context, the categorization of vendors within NelsonHall NEAT projects is as follows:

  • Leaders: vendors that exhibit both a high ability relative to their peers to deliver immediate benefit and a high capability relative to their peers to meet client future requirements
  • High Achievers: vendors that exhibit a high ability relative to their peers to deliver immediate benefit but have scope to enhance their ability to meet client future requirements
  • Innovators: vendors that exhibit a high capability relative to their peers to meet client future requirements but have scope to enhance their ability to deliver immediate benefit
  • Major Players: other significant vendors for this service type.

The scoring of the vendors is based on a combination of analyst assessment, principally around measurements of the ability to deliver immediate benefit; and feedback from interviewing of vendor clients, principally in support of measurements of levels of partnership and ability to meet future client requirements.

Exhibit 1

'Ability to deliver immediate benefit’: Assessment criteria

Assessment Category Assessment Criteria
Offerings Breadth of application of RPA & AI to banking
Application of RPA and AI to retail banking processes
Application of RPA & AI to capital markets processes
Application of RPA & AI to compliance
Application of RPA technology to banking
Application of AI/cognitive technology to banking
Ability to offer new process models with RPA & AI
Ability to benchmark processes and offer roadmap
RPA & AI implementation capability
Ongoing bot/AI management
Combined RPA/people-based exception handling capability
Delivery Scale of RPA & AI delivery capability
UIPath delivery capability
IPSoft delivery capability
Automation Anywhere delivery capability
Blue Prism delivery capability
Cognitive delivery capability
Delivery capability – U.S.
Delivery capability - U.K.
Delivery capability - Continental Europe
Delivery capability – Rest of EMEA
Delivery capability - APAC
Delivery capability - LATAM
Use of pre-existing RPA templates
RPA & AI change management capability
Maturity of RPA & AI delivery model
RPA & AI governance capability
Design thinking capability
Presence Overall banking RPA presence
Overall banking AI presence
Retail banking RPA presence
Retail banking AI presence
Capital Markets RPA presence
Capital Markets AI presence
U.S. presence
U.K. presence
Continental Europe presence
Rest of EMEA presence
APAC presence
LATAM presence
Benefits Achieved Overall banking RPA presence
Level of process cost savings achieved
Process error reduction
Process cycle time reduction
Improved CSAT

Exhibit 2

‘Ability to meet client future requirements’: Assessment criteria

Assessment Category Assessment Criteria
Service Innovation Perceived suitability to meet future client RPA & AI needs
Perceived suitability to develop new banking business models & processes
Ability to apply automation to banking processes
Ability to introduce new digital business models
Service culture
Innovation & creativity
Level of Investments In RPA
In cognitive/AI
In RPA & AI in support of retail banking
In RPA & AI in support of capital markets
In own tools & platforms in support of RPA & AI in banking
In new RPA & AI-based systems of engagement for banking sector
Market Momentum RPA market momentum
AI market momentum

For more information on other NelsonHall NEAT evaluations, please contact the NelsonHall relationship manager listed below.

`/