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Business area digitalisation analysis

Digitalisation of investment and asset management firms

How to connect client suitability, portfolios, trading, risk, reporting and investment decision data

Digital maturity

Typical digital maturity

Shows the level of technological and process digitalisation at which companies in the sector or business area typically operate today.

A typical market situation is assessed, not the most advanced companies.

The assessment consists of five equally weighted dimensions:

Core system usage
Whether ERP, CRM, WMS, MES, customer portals or other operationally important systems are widespread in companies.
Process digitalisation
How many core processes run in systems and how many are still managed manually.
Systems integration
Whether core systems exchange data between themselves or whether employees transfer information manually.
Data quality and readiness
Whether core data is structured, up-to-date, consistent and suitable for automation and analytics.
Advanced data use
Whether real-time analytics, forecasting, automated alerts, optimisation models or AI are used.

The final score is the average of the five dimensions.

1–5 scale

  • 1 very low maturity
  • 2 low maturity
  • 3 medium maturity
  • 4 high maturity
  • 5 very high maturity

A low maturity score does not necessarily indicate low potential. On the contrary, low maturity and a high level of manual work may indicate significant untapped digitalisation value.

High
Skaitmenizacijos potencialas

Digitalisation potential

Shows how much significant business value a typical sector or business area company can create by systematically digitalising core processes.

The rating is calculated on a 100-point scale across five dimensions:

Process frequency and scale 20 %
An assessment of how frequently the digitalised processes recur and what proportion of operations they represent.
Manual work intensity 20 %
An assessment of the extent to which processes depend on email, telephone, Excel, paper documents and repeated data entry.
Impact on revenue and costs 25 %
An assessment of the potential effect on sales, margin, customer retention, administrative costs, errors, downtime or inventory.
Growth and scale potential 20 %
An assessment of whether digitalisation would enable operational capacity to be increased without expanding headcount and costs at the same rate.
Impact on decisions and risk 15 %
An assessment of the potential effect on data reliability, decision-making speed, customer experience, and the reduction of errors and operational risk.

The final score is calculated according to the assessments and weights of all dimensions.

100-point scale

  • 0–20 very low potential
  • 21–40 low potential
  • 41–60 moderate potential
  • 61–80 high potential
  • 81–100 very high potential

A high score does not mean the solution will be easy to implement. It indicates the size of the potential value, not the implementation complexity.

81/100
Biggest challenge
Client mandate and suitability managed separately
Biggest opportunity
Unified investment and client data platform

A business area of high potential and very high data and integration complexity, where one reliable portfolio version is more important than an individual new feature.

Operating model of investment and asset management firms

Asset managers integrate client objectives and constraints, investment research, portfolio construction, trading, accounting, risk control and long-term performance attribution.

Multiple position versions

Trading, accounting, custodian and risk systems may show different states of the same day's portfolio.

Importance of data licences

Market data usage, storage, sharing and AI processing are restricted by source agreements.

Mandate and limit control

Each transaction and portfolio must comply with client, fund, risk and regulatory constraints.

Need for performance attribution

What matters to the client is not only the return, but also the risk, fees, reasons for changes and alignment with their objectives.

Market and technology context

Asset management technologies are being reshaped by unified investment data platforms, automated trade controls, individualised client self-service and source-based AI research assistants.

  • Single trusted portfolio versionPosition, price, transaction, fee and risk data must be reconciled and traceable to source.
  • Automated mandate controlClient and fund restrictions must be checked pre-trade, post-trade and during portfolio monitoring.
  • Individualised reportingClients expect not only numbers, but clear explanations of performance, risk, fees and decisions.
  • Managed AI in researchAI can accelerate source analysis but must show citations, comply with data licences and maintain human validation.

Typical operating process

01

Client onboarding and suitability

Objectives, risk tolerance, restrictions, experience, consents and mandate are collected.

02

Investment research and portfolio decision

Sources are analysed, theses are formulated, models, strategy and transactions are proposed.

03

Pre-trade controls

Mandate, liquidity, concentration, risk and other limits are checked.

04

Trading and settlement

Orders are submitted for execution, trades, positions, cash and depositary data are reconciled.

05

Portfolio valuation and risk

Prices, positions, fees, performance and risk KPIs are updated.

06

Reporting and client service

Client and regulatory reports, commentaries, documents and mandate reviews are prepared.

Digital maturity model for the business area

0

Portfolio and client data reconciled manually

Mandates, positions, prices, fees, risk KPIs, research and client reports are managed across multiple systems, with discrepancies resolved in spreadsheets.

1

Separate portfolio and client data sources

Positions, prices, mandates, research and reports are managed across multiple systems, with discrepancies resolved in spreadsheets.

2

Automated trading but manual reconciliation

Transactions and accounting are digital, but position versions, limit exceptions and report commentary require significant manual effort.

3

Integrated positions, mandates and reports

Core investment data sources are connected, client restrictions are verified, and reports are generated from a managed data model.

4

Data-driven portfolio and client activity Typical current situation

Risk, mandate, tax, performance and client requirement signals are used in the process, and data lineage is visible to source.

5

Adaptive investment platform Siektina

Portfolios are individualised according to managed rules, AI assists in analysing approved sources, and decision and model outcomes are continuously evaluated.

Key finding

The greatest digitalisation value does not come from yet another investor interface, but from a reliable data chain of positions, prices, mandates, risk and reports.

A unified investment data platform is a strategic direction, but the practical first version should be clearly limited – for example, one mandate control and client report.

AI in research and portfolio processes is worth expanding only where sources are licenced, cited, and the final decision and explanation are confirmed by a responsible specialist.

Related digitalisation topics

Investment data platformClient mandate and suitability managementPortfolio risk and limit controlInvestment research AI assistant
Next step

Assessing digitalisation opportunities for investment data and client processes

The data chain of a single mandate, portfolio or client report can be analysed and a first stage defined that would reduce reconciliation and strengthen control.

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