AI has transformed the way businesses handle intricate and dynamic compliance issues. They automate and streamline labor-intensive compliance operations, assist financial institutions to process massive volumes of data, generate predictive insights, and get through the complicated regulatory world more quickly, accurately, and easily.
Let us explore the significant influence of AI on FSCS SCV reporting in this article.
Exploring the Role of AI in FSCS Operations
FSCS (Financial Services Compensation Scheme) embraced artificial intelligence (AI) to help process claims, specifically in the London Capital & Finance plc (LCF) case. The sheer volume of evidence and claims required a more efficient solution, leading FSCS to work with partners like Capita, Capgemini, and Microsoft to develop a system using voice-to-text technology to analyse phone recordings.
This AI-driven approach allowed claim handlers to search for specific keywords and phrases in the text, saving time and effort compared to manually listening to each call recording.
By utilising AI, FSCS could start paying compensation to LCF customers earlier than anticipated, halve processing costs, and save customers time and effort in making claims. Despite initial challenges with accuracy, FSCS improved the AI system through machine learning, ensuring more precise outcomes.
The success of AI in the LCF case has led FSCS to consider its future applications in handling high volumes of claims efficiently and cost-effectively. FSCS continues to review and enhance the accuracy of AI results, aiming to leverage this technology further in claims processing.
How should Banks and FIs update themselves with AI for FSCS reporting?
Educate Staff:
Banks and FIs should educate and teach risk managers, technical teams, and senior management about AI in financial services. This training should encompass an examination of both Predictive and Generative AI, in addition to the risks and mitigation strategies associated with both.
Establish AI Governance and Risk Frameworks:
Banks and FIs must create robust AI governance and risk frameworks to meet financial industry AI adoption issues. These frameworks ought to cover consumer results, data privacy, security, accountability, and openness.
Collaborate with Regulators:
Banks and FIs should work with regulators to comply with changing AI legislation. This partnership can foster an environment that is seamless and effective in the implementation of AI.
Adopt Vendor Governance Processes:
Banks and FIs must implement strong mechanisms to mitigate risks associated with acquiring and integrating third-party Generative AI systems. This applies to the management of data, models, and the risk of misuse associated with third parties.
Incorporate AI into Entire Strategy:
Instead of considering AI as a set of discrete solutions, banks and FIs should use it as a ‘system solution’ to improve analysis and decision-making.
Financial institutions should recognise that AI’s value creation will occur in stages and invest in foundational systems that are in line with the potential value creation across these phases.
AI's Impact on Customer Data: Addressing Inaccuracy, Segregation, Duplication, and Cleaning
Customer data is an invaluable resource for organisations in the era of huge amounts of data. However, upholding precise, clean and structured data can present an immense challenge. AI has become a powerful tool for dealing with prevalent issues such as
Inaccurate Customer & Account Holder Information:
Typos, outdated information, and inconsistent formats can lead to errors and inefficiencies.Account Segregations:
Multiple accounts might exist for the same customer due to different branches, products, or historical mergers.Data Duplication:
Redundant entries can inflate customer counts and skew data analysis.Data Cleaning:
Finding and fixing these issues manually takes time and is prone to error.
Addressing Inaccurate Customer & Account Holder Information
Data Matching and Enrichment:
To find and fix inconsistencies, AI algorithms can compare customer data against a wide range of data sources, including internal databases, social media sites (with permission), credit bureaus, and social media platforms.Anomaly Detection:
AI can analyse data trends and identify anomalies such as strange addresses, phone numbers, or email addresses, urging further inquiry and possible data correction.Natural Language Processing (NLP):
By utilising NLP to comprehend and extract pertinent information from unstructured data sources such as legacy onboarding forms used on various applications, and emails, it is possible to augment customer profiles with supplementary particulars.
Identifying and Addressing Account Segregations
Customer 360 View:
By analysing customer behaviour, transaction patterns, and account information, AI is capable of identifying potential connections between accounts that appear to be unrelated. This process aids in the integration of consumer data into a unified profile, thereby furnishing an extensive view of their connection with the organisation.Clustering Algorithms:
AI can classify clients based on shared traits, purchasing habits, or geographical areas. This could help identify instances where a single person has many accounts and facilitate the merging of segregated accounts.Machine Learning:
The past information can be used to train machine learning models to recognise account segregation patterns and merge duplicate accounts.
Eliminating Data Duplication
Techniques for Deduplication:
AI-driven algorithms can detect and remove duplicate items from several data sets using a range of techniques, such as fuzzy matching and probabilistic record linking.Data Profiling:
AI can create typical client profiles via analysing data attributes. Disparities between these profiles could indicate duplicates needing additional study.Entity Resolution:
AI-based entity resolution can find and merge records for the same entity (e.g., customer) even if reported differently.
Enhancing the Process of Data Cleaning
Automated Data Cleansing:
Repetitive duties such as correcting typographical errors, formatting inconsistencies, and standardising data formats can be automated by AI algorithms.Rule-Based Cleaning:
AI can be programmed with precise rules that detect and rectify prevalent data quality concerns, including invalid entries or missing values.Active Learning:
AI models can evolve by identifying data quality issues and recommending the right cleaning methods.
Optimising the FSCS Single Customer View: A Look at Macro Global's AI-powered Approach
The AI-based algorithms incorporated into Macro Global’s FSCS SCV (Single Customer View) products such as SCV Alliance and SCV Forza play a crucial role in enhancing the efficacy and functionality of the FSCS SCV reporting solutions provided to financial institutions.
Here are some key aspects where AI algorithms are prominently utilised:
Data Accuracy and Validation:
Identify and rectify inaccuracies in customer and account information, ensuring that the FSCS SCV reports are accurate and compliant with regulatory standards.
Data Enrichment and Cleansing:
Help in enriching and reconciling data, thereby improving the overall quality of the FSCS SCV reports.
Automated Compliance:
Validates data against various external databases such as FCA DB, Royal Mail DB, Companies House, and more, streamlines the reporting process, reduces manual efforts and ensures adherence to regulatory standards.
Risk Management:
Identify potential risks and issues within the data through the classification of 170+ SCV audit checkpoints. Helps in addressing high and medium-risk data issues promptly.
Operational Efficiency:
Automate processes like data validation, enrichment, and reconciliation, enhancing the speed and accuracy of FSCS SCV reporting.
AI-based algorithms within Macro Global’s SCV Alliance and SCV Forza serve as a foundational technology that underpins aforementioned critical functions, ultimately empowering financial institutions to meet their regulatory obligations effectively and efficiently.
SCV Alliance
FSCS SCV Audit Platform
