“Noise in machine learning just means errors in the data, or random events that you cannot predict.”

—Pedro Domingos

“Noise” – the quantum of which has grown over the years in the loan processing, is one of the main reasons why bankers have been rooting for automation of loan processing for some time now. The other reason is data integrity, which gets compromised when low-end manual labor is employed during loan processing. In a poll conducted by Moody’s Analytics, when questioned about the challenges they faced in initiation of loan processing, 56% of the bankers surveyed answered that manual collection of data was the biggest problem.

Manual processing of loan documents involves:

• Routing documents/data to the right queue
• Categorizing/classifying the documents based on type of instruction
• Extracting information – relevant data points vary by classification and relevant business rules Feeding the extracted information into the ERP, BPM, RPA
• Checking for soundness of information
• Ensuring the highest level of security and transparency via an audit trial.

“There’s never time to do it right. There’s always time to do it over.”

With data no longer remaining consistent, aggregating, and consolidating dynamic data (from sources such as emails, web downloads, industry websites, etc.) has become a humongous task. Even when it comes to static data, the sources and formats have multiplied over the years, so manually extracting, classifying, tagging, cleaning, tagging, validating, and uploading the relevant data elements: currency, transaction type, counterparty, signatory, product type, total amount, transaction account, maturity date, the effective date, etc., is not a viable option anymore. And adding to the complexity is the lack of standardization in the Taxonomy with each lender and borrower using different terms for the same Data Element.

Hence, the need for automation, and integration of the multiple workflows used in loan origination – right from the input pipeline, the OCR pipeline, pre-and post-processing pipelines, to the output pipeline for dissemination of data downstream. With the added advantage of achieving a standard Taxonomy, at least in your shop.

The benefits of automating certain low-end, repetitive, and mundane data extraction activities

Reducing loan processing time from weeks to days: When the integrity of data is certain, when all data exchanges are consolidated and centralized in one place instead of existing in silos in back, middle, and front offices, only then can bankers reduce the loan processing time from months, weeks to days.

That was what JP Morgan Case achieved with COIN. They saved an estimated 360k hours or 15k days’ worth of manual effort with their automated contract management platform. It is not hard to imagine the kind of impact it had on the customer experience (EX)!

More time for proper risk assessment: There is less time wasted in keying and rekeying data. With machines taking over from nontechnical staff, the AI (Artificial Intelligence) pipelines are not compromised with erroneous, duplicate data stored in sub-optimal systems. With administrative processes streamlined, there’s time for high-end functions such as reconciliation of portfolio data, thorough risk assessment, etc.

Timely action is possible: Had banks relied on manual processes, it would have taken ages to validate the client, and by that time it could have been too late.

Ensuring compliance: By automating the process of data extraction from the scores of documents (that banks are inundated with during the course of loan processing) and by combining the multiple pipelines where data is extracted, transformed, cleaned, validated with a suitable business rules engines, and thereafter loaded for downstream, banks are also able to ensure robust governance and control for meeting regulatory and compliance needs.

Enhances the CX: Automation has a positive impact on CX. Bankers also save dollars in compensation, equipment, staff, and sundry production expenses.

Doing it Right!

One of Magic FinServ’s success stories comprises a solution for banking and financial services companies that successfully allows them to optimize the extraction of critical data elements (CDE) from emails and attachments with Magic’s bespoke tool – DeepSight^TM for Transaction processing and accelerator services.

The problem:

Banks in the syndicated lending business receive large volume of emails and other documented inputs for processing daily. The key data is embedded in the email message or in the attachment. The documents are in PDF, TIF, DOCX, MSG, XLS, form. Typically, the client’s team would manually go through each email or attachment containing different Loan Instructions. Thereafter the critical elements are entered into a spreadsheet and then, uploaded, and saved in the bank’s commercial loan system.

As is inherent here there are multiple pipelines for input, pre-processing, extraction, and finally output of data, which leads to duplication of effort, is time consuming, resulting in false alerts, etc.

What does Magic Solution do to optimize processing time, effort, and spend?

• Input Pipeline: Integrate directly with an email box or a secured folder location and execute processing in batches.
• OCR Pipeline: Images or Image based documents are first corrected and enhanced (OCR Pre-Processing) before feeding them to an OCR system. This is done to get the best output from an OCR system. DeepSight^TM can integrate with any commercial or publicly available OCRs.
• Data Pre-Processing Pipeline: Pre-Processing involves data massaging using several different techniques like cleaning, sentence tokenization, lemmatization etc., to feed the data as required by optimally selected AI models.
• Extraction Pipeline: DeepSight’s accelerator units accurately recognize the layout, region of interest and context to auto-classify the documents and extract the information embedded in tables, sentences, or key value pairs.
• Post-Processing Pipeline: Post-Processing pipeline applies all the reverse lookup mappings, business rules etc. to further fine tune accuracy.
• Output Storage: Any third-party or in-house downstream or data warehouse system can be integrated to enable straight through processing.
• Output: Output format can be provided according to specific needs. DeepSight^TM provides data in excel, delimited, PDF, JSON, or any other commonly used format. Data can also be made available through APIs. Any exception or notifications can be routed through emails as well.

Technologies in use

Natural language processing (NLP): for carrying out context-specific search from emails and attachments in varied formats and extracts relevant data from it.

Traditional OCR: for recognizing key characters (text) scattered anywhere in the unstructured document is made much smarter by overlaying an AI capability.

Intelligent RPA: is used to consolidate data from various other sources such as ledgers, to enrich the data extracted from the documents. And finally, all this is brought together by a Rules Engine that captures the organization’s policies and processes. With Machine Learning (ML) and a human-in-the-loop approach to carry out truth monitoring, the tool becomes more proficient and accurate every passing day.

Multi-level Hierarchy: This is critical for eliminating false positives and negatives since payment instructions could comprise of varying CDEs. The benefits that the customer gets are:

• Improve precision on Critical Data Elements (CDEs) such as Amounts, Rates and Dates etc.
• Contains false positives and negatives to reduce the manual intervention

Taxonomy: Train the AI engine on taxonomy is important because:

• Improve precision and context specific data extraction and classification mechanism
• Accuracy of the data elements which refer to multiple CDEs will improve. For e.g., Transaction Type, Dates and Amounts

Human-eye parser: For documents that contain multiple pages and lengthy preambles you require a delimitation of tabular vs. free flow text. The benefits are as follows:

• Extraction of tabular data, formulas, instructions with multiple transaction types all require this component for seamless pre and post processing

Validation & Normalization: For reducing the manual intervention for the exception queue:

• An extensive business rule engine that leverages existing data will significantly reduce manual effort and create an effective feedback loop for continuous learning

OCR Assembling: Highly required for image processing of vintage contracts and low image quality (i.e., vintage ISDAs):

• Optimize time, cost and effort with the correct OCR solution that delivers maximum accuracy.

Conclusion

Spurred on by competition from FinTech and challenger banks, that are using APIs, AI, and ML for maximizing efficiency of loan processing, the onus is on banks to maximize efficiency. The first step is ensuring data integrity with the use of intelligent tools and business-rules engines that make it easier to validate data. It is after all much easier to pursue innovation and ensure that SLAs are met when workflows are automated, cohesive, and less dependent on human intervention. So, if you wish to get started and would like more information on how we can help, write to us mail@magicfinserv.com.

Wealth managers are standing at the epicenter of a tectonic shift, as the balance of power between offerings and demand undergoes a dramatic upheaval. Regulators are pushing toward a ‘constrained offering’ norm while private clients and independent advisors demand a more proactive role. FinTech Innovation: Paolo Sironi

Artificial Intelligence, Machine Learning-based analytics, recommendation engines, next best action engines, etc., are powering the financial landscape today. Concepts like robo-advisory (a $135 Billion market by 2026) for end-to-end self-service investing, risk profiling, and portfolio selection, Virtual Reality / Augmented Reality or Metaverse for Banking and Financial trading (Citi plans to use holographic workstations for financial trading) are creating waves but will take time to reach critical value.

In the meanwhile, there’s no denying that Fintechs and Financial Institutions must clean their processes first – by organizing and streamlining back, middle, and front office operations with the most modern means available such as artificial intelligence, machine learning, RPA, and the cloud. Hence, the clarion call for making back, middle and front office administrative processes of financial institutions the hub for change with administrative AI.

What is administrative AI?

Administrative AI is quite simply the use Artificial Intelligence based tools to simplify and make less cumbersome administrative processes such as loans processing, expense management, KYC, Client Life Cycle Management / Onboarding, data extraction from industry websites such as SEC, Munis, contract management, etc.

Administrative AI signals a paradigm shift in approach – which is taking care of the basics and the less exciting first. It has assumed greater importance due to the following reasons:

1. Legacy systems make administrative processes chaotic and unwieldy and result in duplication of effort and rework:

Back and middle office administrative processes are cumbersome, they are repetitive, and sometimes unwieldy – but they are crucial for business. For example, if fund managers spend their working hours extracting data and cleaning excel sheets of errors, there will be little use of the expensive AI engine for predicting risks in investment portfolios or modeling alternative scenarios in real time. With AI life becomes easier.

2. Administrative AI increases productivity of work force, reduces error rate resulting in enhancec customer satisfaction

AI is best for processes that are high volume and where the incidences of error are high such as business contracts management, regulatory compliance, payments processing, onboarding, loan processing, etc. An example of how Administrative AI reduces turnaround time and costs is COIN – contract intelligence developed by J P Morgan Chase that reviews loan agreements in a record time.

3. Administrative costs are running sky-high: In 2019, as per a Forbes article, Banks spent an estimated $ 67 billion on technology. The spending on administrative processes is still umongous. From the example provided below (Source: McKinsey) 70% of the IT spend is on IT run and technical debt that is the result of unwieldy processes and silos.

4. Without reaching the critical mass of process automation, analytics, and high-quality data fabric, organizations risk ending up paralyzed

And lastly, even for the moonshot project, you’ll need to clear your core processes first. The focus on financial performance does not mean that you sacrifice research and growth. However, if processes that need cleaning and automation are not cleaned and automated, then the business could be saddled with expensive start-up partnerships, impenetrable black-box systems, cumbersome cloud computational clusters, and open-source toolkits without programmers to write code for them.” (Source Harvard Business Review )

So, if businesses do not wish to squander the opportunities, they must be practical with their approach. Administrative AI for Fintechs and FIs is the way forward.

Making a difference with Magic DeepSight^TM Solution Accelerator

Administrative AI is certainly a great way to achieve cost reduction with a little help from the cloud, machine learning, API-based AI systems. In our experience, we provide solutions for such administrative tasks that provides significant benefits in terms of productivity, time and accuracy while improving the quality of work environment for the Middle and Back-office staff. For banks, capital markets, global fund managers, promising Fintechs and others, a bespoke solution that can be adapted for every unique need like DeepSight^TM can make all the difference.

“Magic DeepSight^TM is an accelerator-driven solution for comprehensive extraction, transformation, and delivery of data from a wide range of structured, semi-structured, and unstructured data sources leveraging cognitive technologies of AI/ML along with other methodologies to provide holistic last-mile solution.”

Success Stories with DeepSight^TM

Client onboarding/KYC

• Extract and process a wide set of structured/unstructured documents (e.g., tax documents, bank statements, driver’s licenses, etc.
• From diverse data sources (email, pdf, spreadsheet, web downloads, etc.)
• Posts fixed format output across several third-party and internal applications for case management such as Nice Actimize

Trade/Loan Operations

• Trade and loan operation instructions are often received as emails and attachments to emails.
• DeepSight^TM intelligently automates identifying the emails, classifying and segregating them in folders.
• The relevant instructions are then extracted from emails and documents to ingest the output into order/loan management platforms.

Expense Management

• Invoices and expense details are often received as PDFs or Spreadsheets attached to emails
• DeepSight^TM Identifies types of invoices – e.g., deal related or non-deal related or related to any business function legal, HR etc.
• Applies business rules on the extracted output to generate general ledger codes and item lines to be input in third-party applications (e.g., Coupa, SAP Concur).

Website Data Extraction

• Several processes require data from third party websites e.g., SEC Edgar, Muni Data.
• This data is typically extracted manually resulting in delays.
• DeepSight^TM can be configured to access websites, identify relevant documents, download the same and extract information.
• Several processes require data from third party websites e.g., SEC Edgar, Muni Data.
• Applies business rules on the extracted output to generate general ledger codes and item lines to be input in third-party applications (e.g., Coupa, SAP Concur).

Contracts Data Extraction

• Contract/Service/Credit agreements are complex and voluminous text-wise. Also, there are multiple changes in the form of renewals and addendums.
• Therefore, managing contracts is a complex task and requires highly skilled professionals.
• DeepSight^TM provides a configured solution that simplifies buy-side contract/service management.
• Combined with Magic FinServ’s advisory services, the buy-side firm’s analyst gets the benefits of a virtual assistant.
• Not only are the errors and omissions that are typical in human-centric processing reduced significantly, but our solution also ensures that processing becomes more streamlined as documents are categorized according to type of service, and for each service provider, only relevant content is identified and extracted.
• Identifies and segregates different documents and also files all documents for a particular service provider in the same folder to enable ease of access and retrieval.
• A powerful business rules engine is at work in the configuration, tagging, and extraction of data.
• Lastly, a single window display ensures better readability and analysis.

Learning from failures!

Before we conclude, an example of a challenger bank that set up an account within 10 minutes, and provided customers access to money management features, and a contactless debit card in record time to prove why investor preferences are changing. It was once a success story that every fintech wanted to emulate. Toda. y, it is being investigated by the Financial Conduct Authority (FCA) over potential breaches of financial crime regulations. (Source: BBC) There were reports of freezing several accounts on account of suspicious activity. The bank has also undergone losses amounting to £115 million or $142 million in 2020/21 and its accountants about the “material uncertainty” of its future.

Had they taken care of the administrative processes, particularly those dealing with AML and KYC? We may never know? But what we do know is that it is critical to make administrative processes cleaner and automated.

Not just promising FinTechs, every business needs to clean up its administrative processes with AI:

Today’s business demands last-mile process automation, integrated processes, and a cleaner data fabric that democratizes data access and use across a broad spectrum of financial institutions such as Asset Managers, Hedge Funds, Banks, FinTechs, Challengers, etc. Magic FinServ’s team not only provides advisory services; we also get into the heart of the matter. Our hands on approach leveraging Magic FinServ’s Fintech Accelerator Program helps FinTechs and FIs modernize their platforms to meet emerging market needs.

For more information about Magic Accelerator write to us mail@magicfinserv.com Or visit our website: www.magicfinserv.com

Money laundering is a crime, a fraudulent activity to cleanse “dirty” money by moving it in and out of the financial system without getting detected. This takes a big toll on banks and financial institutions as they end up paying hefty fines and penalties for anti-money laundering breaches.

Often changes in regulations or sanctions convert otherwise legal money into “dirty” money requiring banks and FIs to report deposits and transactions and also freeze them. Inadvertently releasing these funds could also result in regulatory action.

Constantly changing rules of AML require retraining of staff, changes to workflows, and case tools. Until the staff becomes adept at the new rules, errors and omissions are a huge risk.

A typical money laundering scheme looks something like below.

• Collecting and depositing dirty money in a legal account.
• With banks in the US having a threshold limit of $ 10,000 in deposits scammers deposit lesser amounts to prevent detection using false invoices, made-up names, etc.
• Afterwards, they take out the dirty money via purchases of property and other luxury items through shell companies.
• With this process, money becomes legitimate, and they take out the money from the system.

With regulators across the world coming heavy on any financial institution found negligent of AML compliance, many banks, and financial institutions are turning to machine learning, big data, AI, and analytics for ensuring regulatory compliance and saving themselves the hefty penalties and fines or being named as a defaulter. They are also preventing the disruption to services when costly investigations ensue because of flaws or breaches in AML. Though AML compliance or processing can seem like a gigantic exercise, it is primarily all about collating data and drawing meaningful insights using advanced rules and machine learning.

Quality of data is either an impediment or an asset

Whether it is investigating anomalies, or raising the red flag in time, or ensuring accurate customer profiling (watchlist or sanctions screening), the quality of data is of paramount importance. It is either an impediment that is throwing false positives or an asset which streamlines processes and results in cost effectiveness and efficiency while ensuring compliance.

So, before you proceed with automating AML processing through use of automation tools and machine learning, you need to question –

Is my data clean?

While machine learning has multiple benefits, implementing it is not easy.

1. As underlined earlier – data today is like many headed hydras – emanating from many sources, and in multiple formats – pdfs, invoices, emails, scanned text, spool files, etc.
2. Good data is an asset and bad data an impediment resulting in poor decisions
3. Most of the machine learning technology is all about identifying just the relevant data from terabytes of available data and self-learning over a period of time to become more efficient. However, this needs to be coupled with other technologies to help cleanse the data, and if you are not efficient at cleaning it, you will never get the desired results.

Unfortunately, most data-related work even today is primarily the responsibility of the back- end staff of banks and FIs. The manual process makes it expensive and time consuming. Not just that, human intelligence/capability limits the amount of data that can be optimally processed and hence results in potential errors and exposure.

Result – Delays, late filing of suspicious activity report (SAR), time, resources, and money wasted in investigations, poor customer experience (duplication of effort during Know Your Customer (KYC) and onboarding), potential politically exposed person (PEPS), offenders, and others on the watchlist evading detection, etc. When you fail to spot a suspicious transaction in time or scale up exponentially as per need, you end up bearing the burden of costly fines later.

Magic’s DeepSight^TM Solution raising the bar in fighting money laundering

AI and Machine Learning aided solutions help in finding patterns of unlawful movement of money like layering and structuring, deciphering suspicious activities in time, accurately identifying customers in the sanctions list, transaction monitoring, risk-based monitoring, investigations, and reporting for suspicious activities enterprise-wide. However, the efficiency of these tools is limited by the amount of clean data available. Enter Magic DeepSight^TM , a tool leveraging AI, ML and a host of other automation technologies embedded with Rules Engines and Workflows to deliver extensive amounts of clean data.

Reading like a human but faster: Magic FinServ’s OCR technology and form parsing intelligence use advanced technologies like natural language processing (NLP), computer vision, and neural network algorithms to read like humans and infinitely faster. From tons of unstructured data in the form of text, character, and images, it figures out the relevant fields with ease. What is time-consuming and tedious for the average staff is made easy with Magic DeepSight^TM .

Scaling data cleansing effort exponentially: The importance of cleaning data at scale can be realized from the fact that if it is not done at an exponential pace, machines will end up learning from untrustworthy data. Magic DeepSight^™ leverages RPA, API and workflows to extract data from various sources, compare and resolve errors and omissions.

Keeping track of changing rules: AML rules keep changing frequently, people and entities sanctioned keep changing. In a manual operation, this is bound to cause problems. Magic DeepSight™ leverages Rules Engines where changes in rules can be updated to ensure uniform and complete adherence to new rules.

Identifying customers accurately even when information changes. Digitalization has amplified the efforts that firms have to put in for ensuring AML compliance. Customers move places, they change names, addresses, and other information that sets them apart. It is a tedious and time- consuming affair to keep up to date. Magic DeepSight^TM resolves entities and identifies customers accurately.

Keeping pace with sophisticated transaction- monitoring: Transaction Monitoring is at the heart of anti-money laundering, with sophisticated means adopted by hackers requiring more than manual effort to ensure timely detection. Establishing a clear lineage of the data source is one of the foremost challenges that enterprises face today. Magic DeepSight^TM can read the transactions from source and create a client profile and look for patterns satisfying the money laundering rules.

Act Now! Fight Fraud and Money Laundering Activities

The time to act is now. You can prevent money launderers from having their way by investing right in tools that can do the work of extracting data efficiently at half the time and cost and which can be integrated into your AML workflows seamlessly.

Our research data indicates that 45% of businesses that invested in more AI/ML deployments and had clearer data and technology strategies have fared relatively better in terms of garnering a competitive advantage than the remaining 55% that are still stuck in the experimental phase. Do not take the risk of falling further behind. Download our brochure on AML compliance to know more about our offerings or write to us mail@magicfinserv.com.

Gain a competitive advantage with advanced AML solutions powered by AI and ML  

2021 was a blockbuster year for the regulators. An estimated $ 2.7 billion was  raised in fines or anti-money laundering (AML) and Know Your Customer (KYC) violations in the first half of 2021 itself. The number of institutions that were fined quadrupled from 24 in 2020 to 80 in 2021.

There was a diverse list of defaulters last year, something not seen earlier.  

• There was a bank holding company specializing in credit cards, auto loans, banking, and saving accounts.
• A fintech that achieved phenomenal growth for trading
• A cryptocurrency platform 
  All failed to meet AML compliance standards and were fined.
• Credit Suisse, the Global Investment Bank, was another major defaulter.

All this is an indication that the regulators’ tolerance for default is very limited.

And secondly, the variance in defaulter listing also indicates once and for all that the ambit for AML breaches has widened. No longer confined to the big banks only, post covid-19 all financial institutions must comply with the new reforms enforced by the Financial Crimes Enforcement Network (FinCEN), Financial Action Task Force (FATF), and Office of Foreign Assets Control (OFAC) or face the heat.  

What are the key challenges that banks and fintechs face regarding AML compliance?

Considering that AML observance is mandatory why do banks and financial institutions fail to comply with the standards repeatedly? The biggest challenge remains the overt reliance on outdated systems and processes and manual labor. The others we have enumerated below. 

• Lack of a gold standard for data. Sources of data have grown exponentially and the formats in which they are found have diversified over the years. Further, the widespread use of digital currency has increased the risks of money laundering phenomenally.
• Outdated and incomplete documentation: Data has grown prolifically. This makes customer profiling and integrating data from multiple sources more demanding than ever before. In the absence of automation, it becomes a time-consuming exercise. Most systems used for AML processing are extremely limited in scope and cannot scale as rapidly as desired or have clarity in terms of data accuracy.

• Gaps/flaws in AML-IT infrastructure and false positives: As evident in the instances of NatWest which risked censure for failing to make their systems robust, failure to raise timely alerts could be expensive. However, if the AML systems are not able to distinguish between illegal and legitimate transactions and notify good transactions as well, it loses its veracity. False positives result in duplication of effort and resultant wastage of time.     
• External factors such as WFH and digitization: Rapid advance in digitization and WFH culture post the coronavirus pandemic has increased the threat landscape. Sophisticated means for money laundering like structuring and layering (adopted by criminals/frauds) require exceptional intelligence. However, many of the existing systems are not able to distinguish between illegal and legal transactions, let alone spot activities such as smurfing, where small cash deposits are made by different people. 
• Human factors: When firms are dependent on manual labor, they will face certain typical problems. One of them is the late filing of suspicious activity reports (SAR). Then there is the case of bias and employee conflict of interest. We have the example of NatWest being fined £265 million where employee bias was evident in the laundering of nearly £400 million.
• Investment: Newer and tougher AML reforms call for investment in technology as existing systems are not sophisticated enough. But with the volatility in the market continuing unabated and worsening geopolitical crisis, that is tough.

Simplifying the Complexity of Compliance with Magic FinServ

Magic FinServ brings efficiency and scalability by automating AML operations using Magic DeepSight^TM.

Magic DeepSight^TM is an extremely powerful tool that on the one hand extracts data from relevant fields in far less time than before and on the other checks and monitors for discrepancies from a plethora of complex data existing in diverse formats and raises alerts in a timely manner. Saving you fines for late filing of suspicious activity report (SAR) and ensuring peace of mind.

Customer due diligence: Before onboarding a new customer as well as before every significant transaction, banks and financial institutions must ascertain if they are at substantial risk for money laundering or dealing with a denied party.However, conductingchecks manually prolongs the onboarding and due diligence process, and is the leading cause of customer resentment and client abandonment.

We simplify the process and make it smoother and scalable. Our solution is powered by AI and RPA which makes the AML process more efficient in monitoring, detecting, reporting, and investigating money laundering and fraud along with compliance violations across the organization.

Magic DeepSight^TM scours through the documents received from the customer during the onboarding process for onboarding and due diligence. These are verified against external sources for accuracy and for establishing credibility. Information is compared with credit bureaus to establish credit scores and third-party identity data providers to verify identity, Liens, etc.

Sanctions/Watchlist screening: One of the most exhaustive checks is the sanctions or the watchlist screening which is of paramount importance for AML compliance. The OFAC list is an extremely comprehensive list, that looks for potential matches on the Specially Designated Nationals (SDN) List and on its Non-SDN Consolidated Sanctions List.  

Magic FinServ simplifies sanctions compliance. Our powerful data extraction machine and intelligent automation platform analyze tons of data for watchlist screening, with the least possible human intervention. What could take months is accomplished in shorter time spans and with greater accuracy and reduced operational costs.

Transactions monitoring: As underlined earlier, there are extremely sophisticated means for carrying out money laundering activities.

One of them is layering, where dirty money is sneaked into the system via multiple accounts, shell companies, etc. The Malaysian unit of Goldman Sachs was penalized with one of the biggest fines of 2020 for its involvement in the 1MBD scandal, where several celebrities were the beneficiaries of the largesse of the fund. This was the first time in its 151-year-old history that the behemoth Goldman Sachs had pleaded guilty to a financial violation. It was fined $ 600 million. The other is structuring where instead of a lump sum deposit (large), several smaller deposits are made from different accounts.

Magic DeepSight^TM can read the transactions from the source and create a client profile and look for patterns satisfying the money laundering rules.

Reducing false positives: Magic DeepSight^TM uses machine learning to get better in the game of distinguishing legal and illegal transactions in time. As a result, businesses can easily affix rules to lower the number of false positives which are disruptive for business.

KYC: KYC is a broader term that includes onboarding and due diligence and ensuring that customers are legitimate and are not on Politically Exposed Persons (PEPs) or sanctions lists. Whether it is bank statements, tax statements, national ID cards, custom ID cards, or other unique identifiers, Magic DeepSight^TM facilitates a compliance-ready solution for banks and fintechs. You not only save money, but also ensure seamless transactions, reduce the incidences of fraud, and not worry about poor customer experience.           

What do you gain when you partner with Magic FinServ?

• Peace of mind
• Streamlined processes
• Comprehensive fraud detection
• Minimum reliance on manual, less bias 
• Cost efficiency and on-time delivery
• Timely filing of SAR 

The time to act is now!

The costs of getting AML wrong are steep. The penalties for non-compliance with sanctions are in millions. While BitMex and NatWest have paid heavy fines – BitMex paid $ 100 million in fines, others like Credit Sussie suffered a serious setback in terms of reputation. Business licenses could be revoked. Firms also stand to lose legit customers when the gaps in their AML processes get exposed. No one wants to be associated with financial institutions where their money is not safe.

The astronomical AML fines levied by regulators indicate that businesses cannot afford to remain complacent anymore. AML fines will not slowdown in 2022 as the overall culture of compliance is poor and the existing machinery is not robust enough. However, you can buck the trend and avoid fines and loss of reputation by acting today. For more about our AML solutions download our brochure and write to us at mail@magicfinserv.com.

2022, began with a cautionary note. Stocks slumped and inflation spiked to unprecedented levels worldwide. There was massive disruption of the supply chain due to the pandemic. Just when we thought the worst was over, the breadbasket of Europe, Ukraine was drawn into a devastating war. The uncertainties and geopolitical tensions had a massive impact on the world’s economy – best reflected in the volatility of the stock markets.

It is clear that we are going through uncertain times. That the uncertainties will continue for a long time to come is definite. How must organizations then preempt the challenges lying ahead? What is the key to survival?

In this blog, we’ll attempt to answer these. But first, let us take stock of the primary challenges that organizations will face in 2022. For many, survival will depend on how they tackle the challenges mentioned below.

Key challenges 2022

1. Limited budget and spend: Faced with revenue and growth uncertainties, organizations are limiting spend on non-critical areas. While technology is a leveler, to make the best use of the dollars spent on technology, you must ensure that the processes are optimized first by investing in areas that deliver quick wins rather than aiming for the moonshot.

2. Great attrition and the battle for brains: With more than 19 million American workers quitting their jobs since April 2021, the disruption is massive. But holding on to low- talent employees isn’t effective in the long run.

3. Managing support function: With the WFH culture, the demands on the support function have increased exponentially. Fortunately, most of the time-consuming, repetitive, work in accounts payable, loans processing, KYC, AML, and onboarding can be handled more accurately and cost-effectively with AI and ML, and RPA.

4. Ensuring compliance in WFH: We have seen how the organization’s reputation takes a hit when it falls prey to data breaches as well as compliance failures as was the case with Uber and Panera Bread, where employee carelessness resulted in data breaches. However, an effective cloud strategy and cloud risk management approach navigates risks and improves customer experience. All by driving a collaborative ecosystem.

5. Getting data right: Surveys indicate that nearly a quarter of firms are concerned about fragmented and unreliable data. Though the amount of data has increased manifold times, it is unwieldy and of poor quality.

5. Getting rid of silos – integrating fast: Today one of the biggest problems with data is its existence in silos. You want to make your data useful; you will have to clean it up and structure it. You want to migrate to the cloud; you’d have to know how to make it cost- effective.

2022 would require Enterprises to Adapt, Consolidate, Reinforce with AI, ML, and the Cloud

Data, it is evident, will be playing a defining role in 2022. Whether it be for creating a strong governance framework, or for consolidating systems, data, and processes, or promoting a risk- averse culture. So,

• Organizations must act fast and consolidate and reinforce their key capabilities
• They must become agile and nimble – and learn how to manage their data faster than the others.
• In a highly leveraged world with a fractured supply chain, organizations must get rid of multiple and disparate systems – the silos. They must integrate their processes. This cannot be done without bridging the silos and ensuring last mile process automation.

Magic FinServ: Making Enterprises Agile, Responsive, and Integrated with its IT Services Catalogue, Last Mile Process Automation, and DeepSight^TM

Magic FinServ’s unique capabilities centered around data and analytics and the IT services catalog bring a differentiated flavor to the table and reinforce the organization’s key capabilities while navigating the challenges of data management, broken tech stacks, and scalability.

Our core competence is data while leveraging our cloud and automation capabilities: McKinsey estimates that many time-consuming and repetitive processes like accounting operations, payments processing, KYC and onboarding, and AML along with strategic functions like financial controlling and reporting, financial planning and analysis, treasury will have to be automated. Magic FinServ with its focus on data will be strategic to this initiative.

Comprehensive IT services catalog: We focus on multiple needs whether it be advisory, or cloud management and migration, platform engineering, production support, or quality engineering, DevOps and Automation, production support in an integrated manner to help our customers, whether it be fintech’s or financial institutions, modernize their platforms and Improve Time and Cost to Market.

Domain experience: The fintech and financial institutions’ business landscape is highly complex and diverse. This has been serviced through customized solutions which often create fragmentation and silos. With firms strategically focusing on which core competencies to fortify, you will need a partner that understands the complexities of your focus areas. We bring to the table a rare combination of financial services domain knowledge and new-age technology skills to give you a competitive advantage.

Speedy delivery, minimum dependence on manual effort: From our recent experiences, we know that excessive reliance on manually operated support functions is costly. Our comprehensive last mile process automation tool, Magic DeepSight ^TM , expedites the time required to turn mountainous data into insights, while meeting regulatory standards and ensuring compliance, with minimum human intervention.

Tailored solutions for financial institutions and fintech: Whether it is a KYC, AML, loans processing, expense management, the AI optimization framework utilizes structured and unstructured data to build tailored solutions that reduce the need for human intervention.

Recover costs quicker than the others: For firms worried about spiraling costs, or having no budget allocated for automation and optimization, our solutions, with a payback period of less than a year can be a huge game changer.

Introducing Magic DeepSight ^TM

Compliance-ready solutions: What organizations need today are compliance-ready solutions, as they can no longer afford to invest in building one. Our compliance-ready solution for KYC and onboarding is built for broker-dealers, custodians, corporates, fund admins, investment managers, and service providers and is in accordance with industry guidelines and local, national, and international laws.

Ensuring last mile process automation by speedily bringing all disparate processes into one environment. It is observed that when fintech scales, its IT system is put under immense pressure. As a result, organizations have to deal with disruption. Additional staff are then hired. Increasing costs. With our focus on cloud capability and automation and data-focused services we are in a position to facilitate the last mile process automation. Thereby bridging the gap that still exists in our daily workarounds. Also, DeepSight ^TM , a Magic FinServ platform with AI/ML and RPA at its heart, automates and integrates last mile business processes for improved user experience and enhanced benefits realization.

A precursor of tough times: Act Fast, Act Now!

The current situation is a precursor of tough times ahead. Jamie Dimon, CEO of JPMorgan, said in his annual address to shareholders last year, banks and Financial Institutions needed to adopt new technologies such as artificial intelligence and cloud technology “as fast as possible.”

So, the time to act is now. We understand your problems, and we have a solution to address those. For more information write to us or visit our website www.magicfinserv.com for a comprehensive overview of what we do.