RISK & FRAUD ANALYTICS
Fraud and Risk Analytics Service | design, build and deploy algorithms to detect, assess and prevent Risk & Fraud…
Risk & Fraud Analytics Overview
The siblings Risk & Fraud continue to exist but have grown in complexity, sophistication and technological interventions. The advancements in AI, more specifically in Machine Learning and Deep Learning have made it possible to detect, measure and prevent Fraud.
Trusted Data deploy advanced Risk Analytics techniques to vast amounts of available private and public domain data, so that your business and customers can thrive in a less risky and fraud-immune business envirnoment.
Detection and Prevention of Fraud through analytics
As data has grown exponentially, so has the complexity in classifying, detecting and responding to fraud in a proactive manner. Internal control teams can no longer perform this task effectively on a manual basis.
As organisations operate with large volumes of data, it is critical to implement processes of continuous monitoring, in order to identify data anomalies or behavioral patterns, that are aligned to potentially fraudulent behaviour.
Trusted Data’s client-centric approach to solution design considers the relevant data to acquire, how to efficiently acquire and stream this data, the best analytical tools and techniques to apply in any given fraud scenario and how to effectively embed these tools into an organisation’s fraud risk assessment program.
Ai applications within AML
Historically, financial institutions have relied extensively on manual, human intervention in the regulatory reporting process, with workflows usually embracing physical review, analyses and reports.
As data generation increases in size, complexity and speed, a human mind cannot keep up.
At Trusted Data, we apply advanced data analytics techniques, including such as machine learning, natural language processing and cognitive automation to automate and improve responsiveness in identifying and screening AML threats, reducing operating costs and improving AML detection.
How to Increase Compliance with Analytics
Trusted Data deliver compliance monitoring solutions that incorporate risk-based transactional analytics and workflow delegation capabilities to help clients meet their regulatory requirements and internal compliance controls.
This includes automating fraud detection as part of compliance assurance, or implementing decision-based workflows to meet the unique business rules of a regulated and changing industry landscape.
Lowering Corporate Risk through Ai
Trusted Data have developed advanced risk prediction and simulation solutions, particularly for clients handling account level data, such as in banking and insurance.
Though not exhaustive, our risk analytics solution portfolio supports insurers in setting correct loss reserves, optimising instant claims payouts and identification of subrogation cases.
Within banking, Trusted Data use Ai, amongst other areas, to accelerate risk computation leadtime, improve risk estimation accuracy, predict customer churn and enhance credit scoring capabilities.
In supply chain, we use supplier risk modelling to predict future or incumbent threats to fulfilment, output and cost.
How We Enable Risk & Fraud Intelligence In Your Business
Every Risk & Fraud project is different in respect of goals, data and context. We have helped leading companies become intelligent by delivering solutions that fit each client’s context / needs, leading to quicker insights, lower administrative costs and improved measurement / response to risk and fraud.
How we deliver AML analytics
The data required to tackle financial crime is complex. It either sits across a range of legacy systems or is sitting untaped in the public domain, such as social media.
Financial institutions with large customer portfolios and transaction volumes, spread across various channels and territories face the largest risk. With emerging payment technologies, the challenge to track money laundering has grown.
Traditional AML systems are largely based on static rules that cannot handle large volumes of unstructured data and they therefore struggle to correctly identify questionable individuals, generating a plethora of false positive alerts, often leading to unnecessary additional manpower investment in compliance functions, when the answer actually lies with Ai.
Trusted Data’s AML solution design enables clients to dynamically integrate and analyse traditional and non-traditional data sources, driving increased automation and superior insights in pattern / anomaly detection. The result is lower operating costs, quicker and more accurate AML detection capabilities.
insurance claims analytics
Predictive Analytics in Insurance
Whilst acturial science has done much to propagate analytics maturity in the insurance sector, the competitive landscape has meant that insurance is now viewed as a commodity by customers. Insurance carriers are therefore pressured to find a differentiator. There is a compelling ROI case for leveraging advanced analytics during the claim cycle…It is estimated that a 1% reduction in the loss ratio for a $1 billion insurer can deliver over $7 million additional savings.
Trusted Data’s efforts in the insurance industry focus very much on utilising predictive analytics to model behaviours and draw deeper, more accurate inference of future event probability and risk.
Utilising rules engines combined with text mining, dynamic exception and database searches, we model structured and unstructured data to help clients quickly identify fraud at each stage of the claims cycle.
Our analytics capabilities extend to other key areas in modelling insurance claims data, including the use of text analytics to identify subrogation cases early, optimising limits for instant payouts, ML-driven claims forecasting to adjust loss reserving and clustering loss characteristics to prioritise / assign claims to the most relevant adjuster.
Credit scoring models
Data-driven Credit Scoring
Trusted Data are helping clients move away from more timely and subjective credit scoring mechanism to one that is driven by data intelligence and real-time analytics.
Using Ai, our clients can quanitfy customer portfolio characteristics quickly to then forecast risk as a defined probability.
This helps clients to be less dependent on credit / loan officers and to apply data analytics to become more informed on risk, more consistent in risk profiling, more accurate in risk assessment and more immune to human prejudice and sentiment during the credit scoring process.
tax fraud analytics
Tackling tax fraud with Ai
In a sector where tax refunds often run into tens of billions, predictive analytics can play a key role in assessing the reliability of individual tax returns to mitigate risk.
Trusted Data’s advanced approaches to behavioural profiling and pattern recognition, observe historic characteristics to rapidly learn and determine if they are consistent with present behaviours from a taxpayer.
We can apply sophisticated clustering algorithms to segment a range of common attributes and then dynamically detect unusual patterns or deviations from characterised behaviour. This in turn provides clients with an ability to accurately disseminate honest and fraudulent tax returns.
Debit/Credit card fraud
Managing Card Fraud through Analytics
Fraud detection requires a real time understanding of connections and anomalies among people, transactions, payment methods, devices over time time and by locations
Trusted Data’s machine learning and predictive analytics platforms detect and alert our clients of unusual card transactions, so that they can determine the next best action and curb fraud before it becomes extensive and impacts a banking brand.
In situations such as account takeover fraud, where the resolution time is circa 16 hours, responsivness is critical.
Our sophisticated solutions don’t lie still, seeking out suspicious behaviour 24 / 7, reducing dependence on personnel and offering Fraud functions the ability to respond quickly and work dynamically across multiple time zones.
Identity Profiling and Ai for Fraud
The fraud detection landscape changed rapidly in recent years as fraud patterns change quickly and threat vectors evolve; yet fraud countermeasures are unable to keep pace.
Individual mechanisms such as passwords, biometrics and knowledge-based validation are part of the solution. Combining these measures with multidimensional scoring offers an opportunity to uplift indentity validation measures.
Trusted Data’s analytics portfolio, includes sophisticated approaches to Machine Learning. enabling advances with in-memory event streaming, distributed data processing and continuous learning, offering our clients the ability to: (A) achieve improved risk scoring with low latency, (B) effectively balance big data processing with real-time decision-making, (C) reduce false positives with advanced behavior analysis, which batch learning cannot provide.
Ultimately our focus in identity fraud protection is to build advanced models capable of augmenting human decision-making with increased precision and to reverse engineering machine logic to present human-readable language, better explaining model decisions.
KYC Insights & Compliance
AI approaches including NLP and Machine Learning (ML) are now offering leapfrog opportunities in process automation across large parts of client life cycle management.
At Trusted Data we apply these techniques extensively to support more advanced apporaches to KYC analytics.
This includes intelligent document scanning, extraction of relevant external data sources, improving risk management whilst being mindful of client onboarding in highly regulated sectors.
In the financial sector, we leverage these analytical techniques to manage ongoing changes to regulations and clients risk profiles, in turn helping clients to rapidly identify data and insight gaps in customer information, ensuring continued compliance throughout the client life cycle.
With added pressure in recent years on the detection of ultimate beneficial owners, Trusted Data’s solution capabilities help clients to connect alerts with high quality data, improving their understanding of customer profiles and structures, whilst minimising false positives often brought about by more static modelling approaches.
SPEND & CREDIT ANALYTICS
Applying Data Science within Invoicing & Credit Management
Spend and Credit analytics are key levers for procurement and finance leaders to improve liquidity and shareholder value.
Within spend analytics, Trusted Data are adept at helping clients redefine spend taxonomies, applying advanced text analytics to disparate data to improve spend transparency at the category & vendor level, informing views of global spend, recovery of duplicate payments and reducing dependence on (and cost of) labour intensive cost control functions.
Within credit management, Trusted Data consolidate various data sources from your source systems, such as historical payment data and disputes, integrating this with external data (liquidity, financial stress and credit scores) to:
(A) better anticipate customer payment behaviour, accurately predicting late payments and projected cash flow risks
(B) dynamically define customer risk segments over time
Our focus is ultimately to empower Credit teams to adapt their collection strategies at the customer level, over time, considering for a changing economic landscape and client-specific issues.
FINANCIAL RISK ANALYTICS
Delivering Integrated Financial Risk Analytics
Using the most relevant analytics approaches and technology, including Machine and Deep Learning, Trusted Data address financial risk management by deploying intelligent solutions that measure and manage credit risk, market risk, operational risk and liquidity risk.
When risk factors are exogenous, tackling individual risk sub-components can be the wrong approach. Addressing risk management from a multi-dimensional perspective can mitigate this problem. Trusted Data’s solution capabilities extend to supporting deep dives into investment analysis to provide investor assurance, building advanced insights and recommendations emanating from KYC analytics and extending Ai into credit risk management by enabling lenders to accurately segment credit user risk profiles.
How Trusted Data Deliver Supplier Risk Analytics
Gaining supplier risk visibility beyond tier 1 suppliers is difficult for most organisations. Gaining visibility into tier 2 and beyond, requires an integrated data and analytics apporach, which few organisations have developed.
Trusted Data model supplier risk from different perspectives (including supply lead time, capacity and cost), to offer our clients a more holistic understanding of risks in their supply chain.
This includes building greater visibility into supply networks, tracking supplier relationships beyond tier 1 through external data. Advanced decision support systems enable our clients to configure and analyse vast amounts of data on logistics, quality, product development and capacity to generate leading indicators that can help assess the likelihood and impact of supplier risk events.
Increasing Compliance through Analytics
Compliance professionals face a host of issues and challenges in how to detect risk in the vast amounts of data available to companies.
Acquiring, exploring and and modelling data available from various sources, such as email systems, financial records, sales databases requires deep analytics expertise.
Trusted Data help clients to aggregate and analyse data, through analytics and machine learning, in order to help identify and prioritise compliance-related issues.
By actively attempting to detect unusual behaviour, including corruption, our clients mitigate risk, establish more cost effective data & compliance management practices, whilst building added credibility with governing authorities.
FRAUD NETWORK DETECTION
How to Detect Fraud Networks through Artificial Intelligence
Whilst statistical modelling has been used to run regular fraud detection exercises, with an increase in transactional channels and a shift towards real-time decision making, the need for more agile fraud detection systems is more pressing than ever.
Our fraud detection solutions combine batch analytics, streaming analytics and predictive analytics to establish more dynamic response times and an ability to detect known and unknown forms of anomlous financial transactions.
No two clients are the same and therefore, we customise our solution methodologies in fraud detection based on industry and client context. We help clients define and build parameters around fraudulent behaviour to ensure solution integrity, using advanced approaches to relative scoring, event probability and behavioural clustering; all supporting the end goal of accurate fraud detection.
Our Solution Delivery Model
Define the risk or Fraud cHALLENGE & target performance outcome
Each clients’ risk / fraud challenges have their own, unique dimensions; data, processes, business context and target improvement outcome.
Likewise, Trusted Data’s approach to solution design begins with identifying and defining the right problem for the client and how to clearly define success parameters, which underpin the solution modelling and deployment approach.
ACQUIRE THE right data & ASSIGN THE RIGHT solution methodology
Given the variety and scale of data processed through Financial Enterprise systems nowadays, it is no surprise that related solutions so often fail to deliver exactly what a Client needs.
Once we have defined the right problem, we turn our focus quickly to selection of the right data and modelling methodology, which will deliver your improvement outcome.
Model & Deploy the right proprietary solution to achieve your TARGET outcome
By utilising AI & Machine Learning, we offer Clients added analytical horsepower to comb through the myriad of data which they warehouse, to extract the right insights and outcomes across risk, fraud, and AML.
Rather than ‘selling’ solutions, we focus on the ‘promise of success’, guaranteeing clients superior performance.
Knowledge Transfer, Solution adaptation & impact tracking with the client
We remain to close to clients to support effective knowledge transfer, solution impact tracking and iterative refinements to solutions beyond Pilots or version 1.0
Client needs change and Trusted Data remain repsonsive to adapting solution functionality in line with such requirements.
How We Set Ourselves Apart
Risk & Fraud Analytics
Trusted Data do not operate in the same manner as conventional risk management consulting firms. utilising in-depth knowledge of updated AML / Fraud / Risk legislation from our advisory team, coupled with our deep domain analytics experience in risk modelling, we deliver intelligent fraud / risk analytics solutions that safeguard our clients quicker and more effectively.
- Flexible pricing (gain share or result driven)
- Right| Data, Methodology & Modelling
- Open source tools
- Achieving target for you
- Speedy delivery
- Guaranteed ROI
Our Risk & Fraud Detection Projects
Our advanced analytics and data science team have invested signifcant time working with clients to shape and deploy customised AI solutions to tackle fraud and risk in their business.
Computing Risk in Banking
Dynamic risk computation & classification
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