SYSTEMS AND METHODS FOR ANONYMOUSLY TRACKING AND COMPARING PORTFOLIOS ACROSS THE SIMILAR INVESTMENT PROFILES

Systems and methods for anonymously tracking and comparing portfolios across the similar investment profiles are disclosed. In one embodiment, an investment portfolio computer program may: (1) receive user investment information from a plurality of users; (1) generate a user investment profile for each of the plurality of users based on the user investment information; encrypt the user investment profiles; (3) write the encrypted user investment profiles to a distributed ledger; (4) compare one of the plurality of user investment profiles to the benchmark portfolio; and (5) generate a recommendation for a user associated with the one of the plurality of user investment profiles based on the comparison.

RELATED APPLICATIONS

This application claims priority to and the benefit of Indian Patent Application Number 202011039919 filed Sep. 15, 2020, the disclosure of which is hereby incorporated, by reference, in its entirety.

BACKGROUND OF THE INVENTION

1. Field of the Invention

Embodiments relate generally to systems and methods for anonymously tracking and comparing portfolios across the similar investment profiles.

2. Description of the Related Art

Presently, each user's portfolio is managed in isolation by the user or by the user's portfolio manager. It may be evaluated and benchmarked against an investment strategy that is specified by the user and/or the portfolio manager. It is also applicable to the wealth managers who manages the complete wealth of high net worth individuals and ultra-high net worth individuals.

The current benchmarking process does not provide the user with the ability to get insights into how other users in the same age group and with similar background are investing. This often leads to overlooking certain aspects while defining the investment strategy.

SUMMARY OF THE INVENTION

Systems and methods for anonymously tracking and comparing portfolios across the similar investment profiles are disclosed. In one embodiment, a method for distributed ledger based anonymous portfolio tracking may include: (1) receiving, at an investment portfolio computer program, user investment information from a plurality of users; (2) generating, by the investment portfolio computer program, a user investment profile for each of the plurality of users based on the user investment information; (3) encrypting, by the investment portfolio computer program, the user investment profiles; (4) writing, by the investment portfolio computer program, the encrypted user investment profiles to a distributed ledger, wherein a consensus algorithm operating on a plurality of distributed computer nodes updates the distributed ledger in which multiple copies of the distributed ledger exist across the plurality of distributed computer nodes, and the encrypted user investment profile is added to a block in the distributed ledger according to the consensus algorithm; (5) creating, by the investment portfolio computer program, a benchmark portfolio from the plurality of encrypted investment profiles on the distributed ledger; (6) comparing, by the investment portfolio computer program, one of the plurality of user investment profiles to the benchmark portfolio; and (7) generating, by the investment portfolio computer program, a recommendation for a user associated with the one of the plurality of user investment profiles based on the comparison.

In one embodiment, the user investment information may include a type of investment and an amount of investment.

In one embodiment, the method may further include anonymizing, by the investment portfolio computer program, the amount of investment.

In one embodiment, the user investment information may include a user age, a user income, and/or a user investment sophistication level.

In one embodiment, the method may further include genericizing, by the investment portfolio computer program, the user age and/or the user income.

In one embodiment, the benchmark portfolio may be based on the genericized user age and/or the genericized user income.

In one embodiment, the recommendation may be to purchase an investment that is in the benchmark portfolio but not in the one of the plurality of user investment profiles.

In one embodiment, the recommendation may be to sell an investment that is in the one of the plurality of user investment profiles but not in the benchmark portfolio.

In one embodiment, the method may further include generating, for one of the plurality of users, an investment strategy. The recommendation may be further based on the investment strategy.

In one embodiment, the recommendation may be provided by a trained machine learning engine.

According to another embodiment, an electronic device may include a memory storing an investment portfolio computer program and a computer processor. When executed by the computer process, the investment portfolio computer program may cause the computer processor to: receive user investment information from a plurality of users; generate a user investment profile for each of the plurality of users based on the user investment information; encrypt the user investment profiles; write the encrypted user investment profiles to a distributed ledger, wherein a consensus algorithm operating on a plurality of distributed computer nodes updates the distributed ledger in which multiple copies of the distributed ledger exist across the plurality of distributed computer nodes, and the encrypted user investment profile is added to a block in the distributed ledger according to the consensus algorithm; create a benchmark portfolio from the plurality of encrypted investment profiles on the distributed ledger; compare one of the plurality of user investment profiles to the benchmark portfolio; and generate a recommendation for a user associated with the one of the plurality of user investment profiles based on the comparison.

In one embodiment, the user investment information may include a type of investment and an amount of investment.

In one embodiment, the investment portfolio computer program may further cause the computer processor to anonymize the amount of investment.

In one embodiment, the user investment information may include a user age, a user income, and/or a user investment sophistication level.

In one embodiment, the investment portfolio computer program may further cause the computer processor to genericize the user age and/or the user income.

In one embodiment, the benchmark portfolio may be based on the genericized user age and/or the genericized user income.

In one embodiment, the recommendation may be to purchase an investment that is in the benchmark portfolio but not in the one of the plurality of user investment profiles.

In one embodiment, the recommendation may be to sell an investment that is in the one of the plurality of user investment profiles but not in the benchmark portfolio.

In one embodiment, the investment portfolio computer program may further cause the computer processor to: generate, for one of the plurality of users, an investment strategy. The recommendation may be further based on the investment strategy.

In one embodiment, the recommendation may be provided by a trained machine learning engine.

DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS

Embodiments relate generally to systems and methods for anonymously tracking and comparing portfolios across the similar investment profiles.

Embodiments may allow users to be able to benchmark against the similar investment profiles across the geographies anonymously. For example, users may compare and benchmark the user's assets/investments against those of other users across the globe that may have a similar profile. From this, the user may receive insights as to how his or her investment are faring against the those of similarly situated users. Embodiments may allow wealth managers and users, as necessary, to realign their investment strategies based on the comparison.

Embodiments may leverage smart contracts and may write data to a private or public distributed ledger in an encrypted format. Similarly, any changes by the user or a portfolio/wealth manager may also be written to the distributed ledger. The transactions may be encrypted, and smart contracts residing on the same distributed ledger may decrypt the transactions and capture the transaction details. The smart contract may include logic to generate relative benchmarks to similar portfolios.

For example, when an investor's investment manager buys $50 million worth of stock in Company A, the smart contract will update the investor's portfolio. At the same time, it will also recalculate the generic portfolio index based on the investor's investment. It will attempt to capture a pattern, if any, across the transactions for a given user. This may allow the user and/or investment managers to identify how are they doing compared to similarly situated peers. If they identify that most of users in their early thirties are investing heavily in new emerging markets, user and/or investment managers can quickly review the user's portfolio and realign as necessary.

The use of distributed ledgers and smart contracts provides transparency and anonymity. Users are not tracked nor are any of the investments tagged to any specific individuals. Thus, users should be comfortable sharing their investment details via the distributed ledger.

Embodiments may allow users to benchmark their investments against the users with similar investment profiles anonymously, and may assist users in improvising their investment strategies based on the generic investment trend with the similar profile.

Referring toFIG. 1, an exemplary system for anonymously tracking and comparing portfolio across the similar investment profiles is provided according to one embodiment. System100may include a plurality of users (e.g., user1110, user2120, user3130), each user may have preferences (e.g.,112,122,132), an investment portfolio (e.g.,114,124,134) and investment strategy (e.g.,116,126,136). For example, the user preference (e.g.,112,122,132) may allow the user to specify his or her own investment profile. The user investment strategy (e.g.,116,126,136) may contain the details of the given user's investment strategy based on its investment profile. The user portfolio (e.g.,114,124,134) may reflect the current portfolio view of a given user.

In one embodiment, the user preference (e.g.,112,122,132) may be specified by the user, or it may be implied based on, for example, age, demographics, current liabilities/assets, future goals, past investment decisions, etc.

Trading system140may be any existing trading system that may execute trades.

One or more distributed ledgers may be provided in distributed ledger system150. An example of a distributed ledger is a blockchain-based distributed ledger, and the distributed ledger may be public, private, or permissioned.

The distributed ledger may be built on distributed ledger/blockchain technology. In embodiments, a consensus algorithm operating on a plurality of distributed computer nodes may update the distributed ledger in which multiple copies of the distributed ledger exist across the plurality of distributed computer nodes. Information may be added to a block in the blockchain-based system according to the consensus algorithm.

In one embodiment, the distributed ledger may allow the verified user transactions to be captured and shared anonymously. When any changes happen to the portfolio of a given user, the changes are captured and written to the distributed ledger. The changes may be encrypted.

The distributed ledger may contain the one or more smart contracts145that may capture any changes in portfolios, and generate a view of the relative position among similar investment profiles. The users may be from a similar geographical region, age, income, investment sophistication, etc.

In one embodiment, the system may generate one or more benchmark portfolios that may capture the details about the users' transactions. Each user's portfolio may be compared to one or more benchmark portfolio. The smart contracts145may include intelligence to generate the benchmark(s) and perform the comparison. The smart contracts145may also decrypt the transaction details posted to the distributed ledger by other users.

Referring toFIG. 2, a method for anonymously tracking and comparing portfolio across the similar investment profiles is provided according to one embodiment.

In step205, a plurality of users that are participating in the program may specify their investment preferences. For example, the users may specify a risk profile, preference of market sector, specific region, investment duration (short/medium/long term), types of investment (small/mid/large cap), etc.

In step210, each user's investment profile may be created. Each investment profile may be manually created, may be retrieved from an investment manager, may be retrieved from investment accounts, etc. In one embodiment, the investment portfolio may include the user's holdings, the type of holdings, the duration, the amount, etc. In embodiments, the investment profiles may be based on the user's past investments.

In embodiments, a trained machine learning engine may infer investment strategies from the user's past investments. Users may have an option to override the inferred strategies in order to align them with the user's current strategies.

In one embodiment, each of the investment portfolios may be encrypted and committed to a distributed ledger. For example, a consensus mechanism operating on the distributed ledger nodes may write the portfolios to the distributed ledger as a block in a blockchain.

In one embodiment, before being encrypted, the investment portfolios may be anonymized. For example, any personally identifiable information (PII) may be removed from the investment portfolio. The investment portfolio may be populated with generic information about the user, such as an age range, a geographical region, an income bracket, an investment sophistication level, etc. In one embodiment, the actual investments in the investment portfolios, such as the name of a holding, amount, etc. may be genericized so that the anonymized investment portfolio cannot be used in reverse to identify the user.

In step215, each user may specify an investment strategy. In one embodiment, each user may specify a risk level, type(s) of investments, an investment duration, a value of investment, an industry or sector, etc.

In step220, one or more benchmark portfolios may be created based on the investment portfolios. For example, a smart contract may access the portfolios on the distributed ledger, decrypt them, and generate the benchmark portfolio. In one embodiment, the benchmark portfolios may be created based on a weighted average of user's investment. The weighting may be based on, for example, the portfolio size, the investment amount, the investment history, the user's past investment outcomes, etc. For example, a portfolio for a user with a larger portfolio may receive a greater weighting than that of a user with a smaller portfolio, a portfolio for an experienced investor will receive a greater weighting than that of a novice investor, and a portfolio for a user with a successful investment history may receive a greater weighting than that of a user with a less successful investment history.

In one embodiment, a plurality of benchmark portfolios may be created based on different investor factors, such as age, investment amount, geographic region, investment strategy, income, investment sophistication level, etc.

In step225, one of the users may conduct a trade, and in step230, the trade may be encrypted and committed to the distributed ledger. For example, a consensus mechanism operating on the distributed ledger nodes may write the investment to the distributed ledger as a block in a blockchain.

In one embodiment, the transactions may be written to a distributed ledger that is different from the one that the portfolios are written to.

In one embodiment, any user information associated with the trade may be anonymized before it is encrypted and committed. Trade details (e.g., the specific investment, amount, etc.) may also be genericized as is necessary and/or desired.

In step235, a smart contract, which may be the same as the smart contract that created the benchmark portfolio(s), may update one or more of the benchmark portfolios based on the trade. For example, the smart contract may decrypt the trade, identify the relevant benchmark portfolio(s), and update the relevant portfolio(s).

In one embodiment, if the trade adds a new investment to the benchmark portfolio, the new investment, or a genericized representation thereof, may be added to the benchmark portfolio. Similarly, if the trade removes an existing investment from the benchmark portfolio, the existing investment, or a genericized representation thereof, may be removed from the benchmark portfolio. Similar changes may be made based on modifications to an existing investment.

In step240, a smart contract, with may be the same or a different smart contract, may compare each user's portfolio to the changed benchmark portfolio(s) and/or the trade activity.

In step245, one or more recommendation for each user based on the comparison. For example, the recommendation may be to make a similar trade, to make a different trade, to take no action, etc. In one embodiment, the recommendation may be provided by a trained machine learning engine. The machine learning engine may provide a recommendation based on the user profile and preferences. For example, the embodiments may identify an opportunity to invest in Stock ABC, and the trained machine learning engine may use the user profiles and preferences to determine an investment size, an investment duration, etc. for each user.

In one embodiment, the recommendation may be a report of the trade without any recommended action to take.

The process of monitoring trades, updating benchmark portfolios, comparing, and recommending may be repeated.

Although multiple embodiments have been described, it should be recognized that these embodiments are not exclusive to each other, and that features from one embodiment may be used with others.

The processing machine used to implement the invention may utilize a suitable operating system.