data, artificial intelligence, biddata, python, technologgy

It's time to make All Data Actionable

A “fasten your seatbelts” moment could soon be here. If history is any guide, and depending on the severity of a downturn, large enterprises with weaker balance sheets that have sailed under the radar due to their strong in-year performance could hit the ropes. The current economic climate suggests we are here for a long haul! It's time to make all data actionable.

By contrast, more agile enterprises, who are improving their early warning systems using advanced analytics, creating mechanisms to be able to increase the resilience of their balance sheet may have a better chance to emerge as winners.

Advanced analytics focused on Asset Quality and capital adequacy could prove as valuable guideposts as BFSI enterprises prepare for a turn in profitability.

A closer look at problem loans, loan loss provisions as a percentage of gross loans might play an important role as large and medium-size banks take steps to access their preparedness.

Normally, the approach to problem loans has always been via some mixture of loan workout programs or in worse cases a foreclosure on the underlying collateral.

Loan workout processes during slower economic cycles can take be structured as simple renewal or extension of loan terms, restructuring of loan terms based on simple concessions, or more complex TDRs (Troubled debt restructurings).

A prudent approach in preparing for a downturn will need to focus on developing strategies to:

• Recognize deterioration of credit quality early enough for efficient response.
• Build structures that will signal a decline on time.
• Develop ongoing systems to deal with problem credits as they arise.
• Create an effective plan of action to address troubled situations and maximize recovery.

Banks today have extensive processes and mature capabilities to address some of these issues.

The challenge with these existing processes is that they have not been overhauled to be more efficient with the availability of additional information.

Customers conducting businesses with Banks have expedited the use of information and now process transactions over numerous channels. Some of the new brands for example manage a majority of their transactions via sophisticated online marketplaces while others have dedicated chatbots in place to guide customers.

Does your IT support have processes in place to get a pulse around this new information? Do your early warning systems have the right intelligence to access declining asset qualities and sound an alarm?

Cognino specializes in utilizing explainable AI to augment your existing enterprise analytics. We provide rapid assessment work surrounding critical FISB scenarios and can work hand-in-hand with your existing resources to bring forth impactful insights that will better prepare you in the case of a bad downturn.

Our intelligent CORE platform can easily ingest large volumes of data sets from diverse enterprise systems, monitor social media noise to signal quality of economically impacted customers, provide rapid benchmarking of problem loan metrics with established industry studies, and can help you formulate strategies to mitigate troubled situations and maximize recovery.

Reach out to Cognino.ai and learn how we can augment your core analytics work by pursuing a simple yet sophisticated artificial intelligence strategy to get you prepared and ready to deal better with any potential business outcomes. Let's make our data actionable.


Artificial intelligence, machine learning in finance, machine learning applications in finance, machine learning and reinforcement learning in finance, python, bigdata, machine learning, deep learning, python

Machine Learning in Finance and Insurance Sector

Machine learning or Explainable AI, unlike the conventional AI model, is more likely to predict outcomes with Intelligence, reasoning, and Explainability. The finance sector all over the world is graved with a lot of paper-work, documentation and never-ending government rules and policies.

The presence of AI can be felt everywhere around us, right from the smart speakers to banks who are better able to decide whom to extend a loan to. AI has huge potential to help Financial companies and the Insurance sector make better decisions.

Restructuring the Finance model

The new Physics of Financial Services is Explainable AI. This has restructured the model of the Finance sector by transforming the past building blocks of success into new and better versions which will provide a better process efficiency and will help sustain the cost advantage. Let us summarise and see what these conventional building blocks will be transformed into:

  1. Scale of Assets into Scale of data
  2. Mass production into Tailored Experiences (revenue will be generated from personalized interactions)
  3. Exclusivity of relationships into Optimisation and matching
  4. High switching costs into high retention benefits, and
  5. Dependence of human ingenuity into Value of augmented performance

Though the older model generated revenue but it was always clouded by risks. Now with Explainable AI solutions, success in Financial Services and Insurance sector can be estimated as under:

  1. Companies will no longer require large scale assets for building a successful business, they will require data flow for cost efficiency.
  2. Revenues will not be generated only by standardization of products but it will also depend upon the tailored needs of customers and individual approach.
  3. Exclusive relations will pave under the ability of digitalization to create well-matched connections.
  4. Customers will stay, not because they can walk away, but because their benefits are better there than anywhere else, and
  5. Human and artificial strengths will interplay for better results.

These new building blocks will create an unfamiliar environment that will deliver new kinds of value and reshape operating models.

Use cases of Machine Learning in Finance and Insurance Sector

 The high-end technology of neural networks that make Ai explainable has several use cases in every industry. But here, we will talk about and highlight the most important and commonly applied use cases in the finance and insurance sector.

  1. Know Your Customer (KYC)
  2. AML / Fraud Management
  3. Rogue Employee Detection
  4. Internal Audit Compliance
  5. Tax & Accounting Compliance, and
  6. Compliance Staff Tools

I think we should discuss these use cases in detail

  1. Know Your Customer

Know Your Customer or simply KYC is the process of a business verifying the identity of its clients and assessing their ability. This verification process of the clients also includes the elimination of the potential risks of illegal intentions towards the business relationship.

Explainable AI can play a very significant role in reducing risks of laundering and fraud. Maintaining KYC operations internally leads to :

  • Inefficiencies
  • High cost of ownership

But if we inculcate Explainable AI in KYC documentation, then it will:

  • Improve output quality
  • Increase risk management, and
  • Accelerate counterparty onboarding
  1. AML / Fraud Management

Financial crimes are one of the biggest challenges facing corporations today. It is not defined by geography or type of enterprise. The vast is the complexity of the organization, the more is the threat of financial crime. Financial risk in the banking and insurance sector prevails at every organizational process.

Machine Learning in finance sector here, plays a very crucial role in identifying the underlying threats and risks. Let us have a look :

  • AI can intelligently extract risk-relevant facts from a huge volume of data
  • This makes the process of identifying high-risk clients easier preventing any sort of financial crime, and
  • It can track changes in regulations all over the world
  1. Rogue Employee Detection

Every organization around the world has rogue employees. When we hire employees, we never expect them to go rogue. But the fact of the matter, employees do go rogue. Companies miss many of the impending signs that would help them to prevent the activity.

Explainable AI can very well identify these employees by:

  • continuously monitoring in real-time, the entire employee communications including emails, chat logs, phone recordings, etc.
  • Ai can also track their behavioral patterns
  • Conduct deep contextual analysis.
  • Reducing the risk of document leakage, and
  • Eliminating chances of financial crimes
  1. Internal Audit Compliance

Now, more than ever, the internal audit department is recognized as a key pillar in an organization’s overall governance structure. Unfortunately, past incidents of corporate wrongdoing and, more recently, risk failures have again served to highlight the critical role that internal audit plays and have shone the spotlight squarely on internal audit to step up and deliver on increasing expectations.

By leveraging Machine Learning in Finance sector, an enterprise would be able to achieve:

  • Significant improvements in the quality and effectiveness of their internal audit
  • Automated audit approaches is time-saving for the organization, and
  • Ai systems automatically identify risks, controls, and other key entities within all audit universe documentation.
  1. Tax and Accounting Compliance

Today, organizations are facing increased challenges to handle the ever-changing regulatory landscape and compliance pressure. Functions such as Tax Compliance, Payroll Processing, Consolidation and Financial Accounting demand dedicated time, resources and a high level of

competence.

Tax and Accounting Compliance relies on intensive human capital and is also time-consuming. Companies should adopt an Explainable AI platform to overcome these underlying risks and reduce threats. AI solutions can help the organizations, enterprises, and companies in Tax and Accounting compliance by:

  • Detecting tax computation errors
  • Proposing beneficial tax strategies
  • Enabling dynamic dashboards for sophisticated scenario analysis
  • Automatically categorizing the taxable income into appropriate country-specific tax buckets, and
  • Tax forecasting and reporting
  1. Compliance Staff Tools

Compliance Staff Tools is a new concept that can be structured through the system of Machine Learning in finance sector. AI enables additional support tools for compliance management teams such as:

  • Dynamic dashboards
  • Data visualization tools
  • Case management tools, and
  • Alerting and alert investigation tools

Summary

Role of Explainable AI in the Finance and Insurance sector is far more extensive and massy. Thus, AI helps the organizations in making predictions, outcomes, and decisions which are far more reliable and factual.

The finance and Insurance Sector needs to understand that both the company and AI specialists need to work together to benefit from this upcoming technology which can prove as a boon for them.