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Affinity Risk Model Predict Financial Risk

Financial Risk Demystified: Decoding the Landscape

We know one thing common amongst banks and financial institutions of any size, nature and complexity is their aversion towards financial risk. In addition to this, we can also affirm that all of them have a greater yearning towards putting better measures to determine such financial risks. The traditional ways of dealing with risk prediction have utilized data but leverage conventional techniques that give a discovery of the specific entity only, not the interlinked risk scenarios. This was quite helpful in some cases but fails to provide a true representation of the risk scenario that a bank or a financial institution was exposed to when dealing with financial instruments across varying levels of complexities in terms of construct, reach, scale as well as interactions including a variety of associated entities, financial institutions, locally as well as globally.

To add to this, we live in a connected world, which has allowed fraudsters to manipulate this overly complicated, interwoven financial environment including several kinds of regulatory and compliance practices to commit frauds that are increasingly becoming difficult and time-consuming to assess and discover using traditional techniques. All these have put immense levels of strain on already constrained resources available within the banks and financial institutions, thus demanding for smarter ways of dealing with risk prediction, one of them is using Machine Learning and AI-based modeling.

One of the key elements that is at the epicentre of this specialized approach is “Data”, which is an asset that banks and financial institutions have been heavily investing in over the years. Thus far, we have seen large investments been done in building data infrastructure that provides the capability to these institutions to not only capture, store and manage but also harness the value out of the voluminous transactional data. The ability of Machine Learning models to analyze large amounts of data both financial and non-financial – with more granularity and deeper analysis – can improve analytical capabilities in risk management and compliance, helping institutions make more informed decisions in predicting risks.

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A Holistic Risk Assessment ParadigmMachine Learning and AI-based models allow financial institutions to better assess risk not only at a granular (individual account) level but it also extends it to assess the risk at a much broader systemic level in a far efficient manner resulting in improved ROI. The usage of data from unconventional areas along with conventional financial data (which forms Big Data) in Artificial Intelligence offers new potential risk management tool.

With the significant increase in the number of frauds in recent times, banks and financial institutions have to fundamentally reshape their risk management capabilities. The existing risk assessment methods are restricted in identifying financial risk associated with an entity in a standalone manner. In reality, it is not only based on the historical financial behaviour alone, but also the social and lifestyle (in case of individual) behaviour. Most banks and financial institutions globally have made huge investments in building their data infrastructure in recent years so that they can be efficient and intelligent in running their operations.

Analytical models enable the banks to be predictable in their business decisions. Risk officers are trying to take a wide angle view of their customers, employees, intermediaries to observe patterns in financial and social behaviour. Deep Learning and Data Analytics can decode the hidden risks due to the behaviour and their affinities and enrich risk assessment. Based on the enhanced risk assessment and behavioural aspects through data science, financial fraud can be predicted. This can assist in Anti-Money Laundering (AML) as well.

To manage risk effectively, banks and financial institutions have to go beyond the regulatory compliances and look at non-financial and non-traditional data.

In A Nutshell: Connecting the Dots

By leveraging Data Science - AI / ML capability, the accuracy of predicting and identifying risk has increased significantly.

Conventional practices in banking technology are unable to predict financial risk more effectively due to a siloed approach.

R I S K

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How it Enriches A Bank’s Risk Assessment?

Decoding Risk: The ARM Way

A bank or financial institution’s risk assessment and impacted risk is assessed based on the interdependencies each affinity has. Furthermore, the accounts and the relationships can be mapped and filtered by the bank as per the needs and requirements across 4 levels.

The model helps in advancing potential risks, thereby enabling the banks to do a thorough due diligence and create affinity- based red flags and the proportional risk intensity each of the affinity poses. Let’s here look at how the model helps in the financial risk discovery.

Enrich Risk Assessment : A Sneak Peek into Affinity Risk ModelNihilent’s Affinity Risk Model takes into ambit a 360-degree view of customers and the interlinked risk. This model will enhance the risk assessment capability that the bank or a financial institution currently has and can provide greater insights about financial behaviour. The model’s inherent strengths lie in its ability to slice and dice the data for an enriched risk outcome. The model’s ability to crawl across affinities and filter the unique risk perspectives associated with each of affinities, its interdependencies and its impact are granulated. With the judicious blend of Data Science -AI / ML-driven model gives a holistic picture of the risk patterns and empowers the banks and financial institutions to make informed decisions leading to a well-meshed risk management paradigm.

Enhanced Risk Assessment

Greater Customer Intelligence Based On Their "Affinity”

Proactive Risk Mitigation

Granulating the Affinities and the Interdependencies

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The Affinity Risk Model shows the type of “Affinity” the different accounts have. Also, it shows how much the “Affinity” influences the risk of the other accounts or vice-versa based on the financial behaviour on each account.

The Solution Walkthrough

The first level of the dashboard provides the health summary of the accounts with respect to risk. There are three categories:

Safe Accounts

Moderate Risk (as per the bank’s benchmark) and are on watch list; bank may take some proactive actions

In this instance, out of 100 accounts, 48 accounts are in safe zone, 41 accounts are in moderate risk and bank may take some proactive actions; 11 accounts are in high risk and bank should definitely take some action. The second level on the dashboard indicates the accounts that have transited into different risk zones This makes for continuous surveillance of the risk and its classification.

Account Risk-Wise Drill-Down List

This is the drill-down list of the specific risk category of the accounts. The user will also have an option to select specific account type within the risk category for the drill down. If the bank wants to view which 48 accounts are in safe zone, they can right click on the bar and drill through account rating data.

Affinity Between Accounts

This provides the type of affinity between accounts. Nihilent has designed “Affinity” between accounts. For instance, the “Affinity” between A0000000074 and A0000000025 can be explained as the account holder A000000025 is introducer of the account A000000074. The “Affinity” at every level is compounded to identify the final “Affinity” between any two accounts.

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High Risk Accounts (as per the bank’s benchmark) and which require some pro-active / immediate actions before it explodes

Account Impacted Risk Rating

A snapshot of risk across levels for an account due to “Affinity”. This helps the bank to take informed risk mitigation choices.

Level 0 is the account on query for which the affinity and the impacted risk are to be provided.

Level 1 displays all those accounts which has direct “Affinity” with the account in query.

Level 2 displays all those accounts which has direct “Affinity” with the accounts identified in Level 1 above.

Level 3 displays all accounts which has direct “Affinity” with the Level 2 accounts as above.

Level 4 displays all accounts which has direct “Affinity” with the level 3 accounts as above.

Bank’s own risk rating Nihilent’s assessed enriched risk rating

© 2020 Nihilent Ltd.* The level definition is with respect to level 0 account i.e. Account in query

Across geographies, Nihilent has enabled transformation at some of the leading banks of the world with a judicious blend of strategy and technology for a delightful customer experience. For one of South Africa's Big Four in banking, Nihilent ushered in ground-up transformation by transitioning the bank’s conventional operations to digital banking.

Take the case of one of India’s diversified financial services organization having a pan-India footprint across 1,400 locations and 20,000 employees, to transition to the next level of banking tech maturity, Nihilent helped the bank deploy a highly scalable elastic Cloud Datawarehouse solution.

In another instance, a leading Indian bank with 1,000 branches and 1,885 ATMs spread across 625 locations in India, Nihilent helped the bank on its transformation journey by reconciling the existing data warehouses and by designing and implementing a new data warehouse on the Cloud.

Today every bank is more of a technology organization, our digital solutions are helping the banks and financial institutions to stay ahead in the game. Through our unique Design Thinking approach, we pride ourselves at being able to give our customers a truly unique and well-thought-out solution customized to their needs. Providing clients with greater insights, higher transparency, cover risk, improved customer experience, and reduced cost all the while keeping information secure.

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Expertise inFinancial Technologies

Offerings: Deep Dive

BlockchainNihilent has been one of the early adopters of Blockchain technology, and we have actively engaged with customers across the globe to identify the right use cases for business problems that can be solved using the Blockchain platform. Our solutions like- Proxy voting, Smart insurance, No Claim Bonus (NCB) are well known among banking professionals.

Nihilent is the technology partner for the Blockchain initiatives around Insurance Chain. A leading general insurance company was struggling to manage its NCB with numerous challenges like timely availability of data, delayed policy disbursals, operational inefficiencies among others. By Nihilent’s Blockchain insurance chain, the company accrued overall uptick in its operational efficiencies like instant access to claims data, effective premium management, better insight into claims discovery and significant cost savings leading to higher customer satisfaction.

Mobile Wallet and PaymentsWith our mobile wallet and payment solutions, we enable you to deliver seamless and personalized experiences to your consumers- anytime, anywhere.

Nihilent has proven expertise in powering payment solutions. For a leading platform provider of mobile financial services based out of South Africa, a mobile wallet was developed. This has been implemented in banks and telecommunications companies across 40 countries including providing services for integration to a wider payment ecosystem.

Cognitive Analytics Based Customer Satisfaction Analysis Equipped with some of the advanced image and video processing algorithms, our cognitive analytics solution understands emotions and precisely captures even the subtler cues that are hard to be noticed by the naked eye. Using this can help know, how satisfied the customer you served was, and allows you to have an insightful feedback mechanism in place.

If we contrast this backdrop with a real-life case, Nihilent helped a leading bank by analyzing customer emotions using Microsoft Cognitive APIs and granulated the overall branch customer satisfaction index.

Financial Crime Analytics With the emergence of newer technologies, the risk of one becoming a victim of financial crime is ever-increasing. You can now choose to stay secure and prevent financial crime from happening by leveraging our analytics solution which facilitates detection, investigation, reporting, and mitigation of fraud and other financial crimes.

Take the case of one of Africa’s well-respected crime information center effectively leveraging public and private partnerships to deliver distinct value to its customers. The client wanted to enhance its value proposition and wanted to leverage data and information assets for fighting financial crime by member banks. Pitching in, Nihilent developed a ‘Unified Data Architecture’ across business areas such as App Building, Facial Recognition and Social Media Analytics.

Branch Analytics We enable you to effectively investigate the branch’s performance. Insights from this analysis can be leveraged by CXOs in ensuring alignment with the strategy of the organization and in empowering their managers to make informed decisions in all the operational activities.

Credit Default PredictionAn analytics solution that predicts the probability of credit default based on the characteristics exhibited by the end-users. Various machine and deep learning models are employed to boost the accuracy of our solution and to present dependable results for our customers. These services are mostly embedded in many large banking transformation engagements Nihilent undertakes.

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Nihilent is a global consulting and services company that uses a human-centered approach for problem-solving and change management. Nihilent's comprehensive range of expertise in business process and technology enables customers to achieve newer heights of business performance. Our drive for performance change is through Innovation, Transformation, and Optimization.

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Atalanta Kar

atalanta.kar@nihilent.com

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