Asset Management
Fund reporting and peer comparison, built as an AI employee. Cockpit, peer positioning and reporting, prepared for fund management, and every statement openly carries its origin and resilience.
Funds and mandates are reviewed at the agreed cadence: key figures, peer comparison and anomalies are held per fund in a cockpit. The risk metrics for fund reporting are compiled and prepared according to the definitions of your management company; the AI employee does not make the risk classification itself.
Each fund is read against its peer group: performance, costs, volatility and fund flows, with origin and resilience for each figure. The peer group comparison of the funds evaluates publicly available comparison data for your own positioning; scoreprise does not assign a rating or ranking.
The AI employee carries out preparatory work in reporting: fund reports, committee papers and the input for the master management company reporting are held ready in draft form, from the same checked data as the cockpit. For sales management in asset management, fund flows in fund distribution are evaluated per distribution partner and period. Approval remains within the company.
Partnership
In partnership with Morningstar.
There is a collaboration with Morningstar on the data side: financial data is made usable for AI roles, and from this jointly developed use cases for wealth and asset managers have emerged, from portfolio screening through research automation in asset management to advisory documentation. Together, both firms serve clients in wealth and asset management: Morningstar contributes research, ratings and market data, scoreprise.AI the AI roles that turn this into ongoing work. No company relies on a single data supplier, which is why the AI employee works across several providers, and every statement openly carries its origin and its resilience.
Fund cockpit and look-through
Look-through per fund, the exception goes to fund management.
For each fund, the AI employee brings together key figures, peer comparison and risk profile and checks them against the agreed corridors. The look-through analysis shows which positions lie beneath a target fund, and discrepancies between fund accounting and the data provider are flagged as a data quality question before they reach a report. Only the cases that require a decision go to fund management, with classification and reasoning.
Book a callClassification: position against the peer group and the previous quarter, with a reason for each change. The origin and resilience of the underlying data are shown for each metric.
Fund reporting software and SFDR reporting
Fund report and SFDR reporting, drafted ahead of the committee.
Fund reports, committee papers and the input for SFDR reporting come from the same verified data as the cockpit. The AI employee prepares drafts from metrics, peer comparison and market conditions; for ESG reporting for asset managers, sustainability data is brought together for each position, as far as the data providers supply it. Fund management reviews, amends and approves.
Book a callThe limit
Rating and risk classification are deemed high-risk under the EU AI Act. That is why the division of labour is clear.
The AI employee takes on
- Screening funds and drafting classifications
- Calculating and flagging scenarios
- Drafting fund reports and committee papers
- Calculating and flagging peer comparisons
The following remains with your company
- The statement made to investors and committees
- The investment decision
- The reporting obligation
- Every approval at the critical points
The starting point is internal and with fund management. This is not risk management software for the KVG under the KAGB: the risk management system remains the regulated in-house function of your KVG, the AI employee brings together the metrics you have defined and prepares them; existing AIFM risk management software is connected as a source. There is a partnership with Ernst & Young as implementation partner for the wealth and asset management market, and a collaboration with Morningstar on the data side.
Your fund and research data in the tools your fund management already uses. The answer comes from the same verified basis.
Check us before you speak to us.
Three ways to check our work in advance: the comparison of our features with the largest providers, documented cases from other companies and a check of how robust your market view is today.
Provider comparison
128 features of the fund cockpit, feature by feature against the largest providers of asset management software. The methodology is disclosed.
Best practices
How companies across various sectors have brought market and sales intelligence into their operations: for each case, the task, the sources and the output format. The collection is growing.
Maturity check
How resilient is your fund reporting? Four levels from gut feeling to evidenced: the origin of each metric, the cadence, peer comparison from public data and pre-drafting for the committee and SFDR.
Frequently asked questions
Does the AI employee make investment decisions?
No. It provides preparatory work, classification and reasoning. The statement to investors and committees, the investment decision and risk management remain with your management company. Rating and risk classification count as high risk under the EU AI Act, which is why the starting point is fund management.
What requirements apply to SFDR Article 8 fund reporting?
Article 8 funds disclose which environmental or social characteristics they promote and how these are met, in the prospectus, in the annual report and on the website in accordance with the SFDR technical standards. The AI employee brings together the information required for this per position and pre-drafts the report sections; content and approval are the responsibility of the management company.
What AI use cases are there in fund management?
Before the decision: a fund cockpit with metrics and corridors, peer positioning from public comparison data, look-through per fund, pre-drafting of fund reports, SFDR and committee papers, analysis of fund flows in distribution. The investment decision, rating and risk classification remain with fund management.
Does this meet the requirements for a management company's risk management under the KAGB?
Risk management under the KAGB, including functional separation and risk limits, is the management company's own function and remains there. The AI employee provides the consolidation and preparation of the risk metrics you define for reporting, with the origin and status of each figure.
What does DORA mean for scoreprise as an ICT third-party provider?
As an ICT service provider to a financial company, we are subject to the contractual requirements arising from DORA: service description, data processing locations, access and audit rights, exit arrangement. Operation in the EU, logging of every work step and the machine-readable handover at the end of the contract are part of the contract; the classification as a critical service provider is made by your company.
Where does the data come from?
From several data providers, because no company relies on a single rating or data supplier. On the data side, there is a collaboration with Morningstar. Every statement openly carries its origin and its resilience.
How does the build start?
With a first conversation and a scoping workshop, which produces a role profile with a target profile, signals, volume estimate and delivery format. The build starts with one case, validated with the responsible specialist, then scaled. The contract can be cancelled monthly.
Where does operation take place?
Entirely in the EU. Transmission is encrypted, access is role-based, every step of work is logged.