Data Analytics for Financial Services
Global technology and market trends including the rise of AI, regulatory developments, market volatility driven by geopolitical changes, ESG mandates and the quantum of data surging to zettabytes are creating a complex operating environment for the financial services sector.
To adapt to the new normal and gain a competitive edge, CXOs of financial institutions across the globe are actively implementing real time data-driven strategic and transformative changes within investment banks, commercial banks, asset management firms, hedge funds and private equity firms.
Acuity’s Data and Technology Services (DTS) practice has been a trusted partner to financial services since 2005. With this contextual partnership, our clients have been able to differentiate offerings, enhance competitiveness with predictive analytics, take informed decisions to generate alpha, and redefine customer experience, thus making them operationally efficient.
Financial Data Analytics Services
From scalable data ingestion to contextual insight-covering
the entire data lifecycle
Data Infrastructure
- Data discovery
- Data ingestion
- Data pipeline
Data Transformation
- Data processing
- Data tagging
- Data architecture
Data Analytics
- Advanced analytics
- BI / visualization
- AI / machine learning (ML) solutions
Our Data Management Framework
for Financial Services
ACQUIRE
- Traditional data: Financials, market data, operational metrics and macroeconomic
- Proprietary data: In-house data lake and subscribed data sources
- Alternative data: Individuals, business and machines
PREPARE
- Data transformation: Cleansing, structuring, enrichment, normalising, data splitting and tagging
- Exploratory data analysis: Identifying missing values, duplicates and imbalances
- Features Engineering
ANALYSE
- Supervised machine learning
- Unsupervised machine learning
- Deep learning
- Natural language processing and unstructured data mining
- Stochastic/statistical modelling
CONSUME
- Bespoke reporting: Data, dashboards, data lakes, bespoke emails and reports
- Proprietary outputs: Signals on client-defined criteria and bespoke indices
- Insights that are descriptive, diagnostic, predictive or prescriptive in different contexts
Our Technology Expertise
Snowflake
Databricks
Python
AWS
Power BI
Tableau
SAS
R Programming Language
Google Cloud
Microsoft Azure
Looker Studio
Why Acuity?
Domain Centric
1000+ technologists work closely with 4,800+in-house research experts to create bespoke and contextual outcomes across asset classes
Pragmatic RoI-Driven Approach
Meticulous scoping and solutioning process to identify key client challenges and formulate a milestone- and incremental gain-based framework with clear timelines
Robust Solution-Oriented Framework
Our data analytics framework is modular across the data acquisition, preparation, analytics and reporting stages, thus enabling a tailored approach that can unify or overhaul existing processes to drive efficiency.
Tailored Solutions for Banks,
Asset Managers, and Private Markets
Banking
- Re-engineering and transforming credit risk data of underlying portfolios for automation and cost reduction
- Machine learning (ML) for advanced Equity Capital Markets (ECM) Analytics delivering predictive equity issuance decisions
- Customer attrition analytics, product strategy analytics, branch analytics and consumer household insights for consumer banks
Asset Management
- AI-powered ESG analytics for portfolio managers, with automated coverage of holdings
- Select and analyse large volumes of alternative data to create differentiated, value-added and anti-consensual research
- Bespoke algorithms and baskets to facilitate thematic investments
- Accelerate alpha research with in-depth reporting pg alpha performance
- Non-linear factor modelling for systematic investment strategies
Private Market
- AI-powered automation of deal origination and due diligence to optimise time and costs
- Pricing analytics to optimise commercial strategies for portfolio companies
- Scalable target screening and anomaly detection using alternative data
- Identify emerging investment themes using AI
- Analyse shareholding and trading patterns to derive exit intelligence
- Pre-investment analysis for optimal deal sourcing
Our Perspective
Unlocking Alpha with Alternative Data: Generating Unique Investment In..
Decoding transactions – a deep dive into enterprise card payment ana..
Leveraging personality traits to gain an advantage in marketing
Technology integration into SUPPLY CHAIN MANAGEMENT
Alternative data steadily building momentum in lending
Cross-Database Union in Tableau
Unlocking Alpha with Alternative Data: Generating Unique Investment In..
Decoding transactions – a deep dive into enterprise card payment ana..
Leveraging personality traits to gain an advantage in marketing
Technology integration into SUPPLY CHAIN MANAGEMENT
Alternative data steadily building momentum in lending
Cross-Database Union in Tableau
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Frequently Asked Questions
How does Acuity support Data Analytics for Financial Services?
Data engineering supports financial institutions by managing vast amounts of data amid AI advancements, regulatory changes, market volatility, and ESG mandates. It aids strategic transformations in investment banks, commercial banks, asset management firms, hedge funds, and private equity firms. Acuity’s Data and Technology Services (DTS) practice, a trusted partner since 2005, enhances competitiveness, differentiates offerings, and drives efficiency gains. Effective data management and analytics help generate alpha and improve overall performance and profitability, enabling financial institutions to navigate complex environments and maintain a competitive edge.
What is the role of machine learning in financial services?
Financial institutions use machine learning (ML) models to develop trading algorithms that can analyse vast amounts of data and execute trades at optimal times, improving profitability.
Credit Scoring: Machine learning models assess credit risk by analysing historical data, enabling more accurate and faster credit scoring and lending decisions. Chatbots and virtual assistants powered by machine learning also provide efficient customer service, handling inquiries and transactions without human intervention. Machine learning also helps in identifying and mitigating risks by analysing market trends and predicting potential financial downturns.
What is ESG analytics, and why is it important?
Acuity Knowledge Partners supports ESG analytics by offering customised research and advisory solutions tailored to the specific needs of financial institutions. The services include building comprehensive ESG databases, developing frameworks and models, conducting risk assessments, and performing opportunity analysis. Acuity also assists with climate financing, disclosures, and reporting. It leverages AI and NLP-driven solutions to enhance their ESG analytics capabilities, ensuring accurate and efficient assessments. This holistic approach helps clients navigate ESG risks and opportunities, align with global standards, and achieve their sustainability goals.
How does Acuity Knowledge Partners support data transformation in financial services?
Acuity Knowledge Partners supports data transformation in financial services by offering comprehensive data engineering and analytics services. It provides scalable solutions for data ingestion, transformation, and analysis, covering the entire data lifecycle. Other services include data discovery, cleansing, structuring, enrichment, and tagging, as well as advanced analytics using AI and machine learning. Acuity’s Data and Technology Services (DTS) practice helps financial institutions enhance competitiveness, generate alpha, and drive efficiency gains by leveraging contextual insights and bespoke reporting. This holistic approach enables clients to navigate complex financial environments and achieve strategic goals.
How can financial firms stay competitive with the rise of AI?
AI can be leveraged to enhance customer engagement, improve decision-making, and streamline operations. Implementing AI-driven solutions like predictive analytics, anomaly detection, and natural language processing can help firms manage compliance and provide personalised customer experiences. This can be a part of adopting an “AI-first” approach, thus transforming core technology and data infrastructure, and fostering a culture of innovation. By integrating AI systematically across all business processes, financial firms can maintain a competitive edge in a rapidly evolving market.